NKPC_AR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pystan\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>chingin</th>\n",
       "      <th>years</th>\n",
       "      <th>Qr.</th>\n",
       "      <th>year_q</th>\n",
       "      <th>chingin_r</th>\n",
       "      <th>chingin_r4</th>\n",
       "      <th>unemp</th>\n",
       "      <th>CPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1970</td>\n",
       "      <td>1st Qr.</td>\n",
       "      <td>1970_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>32.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1970</td>\n",
       "      <td>2nd Qr.</td>\n",
       "      <td>1970_2</td>\n",
       "      <td>1.042553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>32.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20.4</td>\n",
       "      <td>1970</td>\n",
       "      <td>3rd Qr.</td>\n",
       "      <td>1970_3</td>\n",
       "      <td>1.040816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>32.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>4th Qr.</td>\n",
       "      <td>1970_4</td>\n",
       "      <td>1.029412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>33.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1971</td>\n",
       "      <td>1st Qr.</td>\n",
       "      <td>1971_1</td>\n",
       "      <td>1.052381</td>\n",
       "      <td>1.175532</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  chingin  years      Qr.  year_q  chingin_r  chingin_r4  \\\n",
       "0           0     18.8   1970  1st Qr.  1970_1        NaN         NaN   \n",
       "1           1     19.6   1970  2nd Qr.  1970_2   1.042553         NaN   \n",
       "2           2     20.4   1970  3rd Qr.  1970_3   1.040816         NaN   \n",
       "3           3     21.0   1970  4th Qr.  1970_4   1.029412         NaN   \n",
       "4           4     22.1   1971  1st Qr.  1971_1   1.052381    1.175532   \n",
       "\n",
       "      unemp        CPI  \n",
       "0  1.066667  32.066667  \n",
       "1  1.133333  32.533333  \n",
       "2  1.233333  32.600000  \n",
       "3  1.266667  33.500000  \n",
       "4  1.166667  34.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=pd.read_csv(\"chingin_emp_CPI_q.csv\")\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d4=d[4:]\n",
    "d4=d4[:-1]\n",
    "data={\"N\":len(d4),\"pi\":d4[\"chingin_r\"],\"u\":d4[\"unemp\"]}\n",
    "d4s=d4[:40]\n",
    "sdata={\"N\":len(d4s),\"pi\":d4s[\"chingin_r\"],\"u\":d4s[\"unemp\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats.mstats import mquantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runmodel(model,data):\n",
    "    smodel = pystan.StanModel(model_code=model)\n",
    "    fit = smodel.sampling(data, iter=31000, warmup=1000, thin=10, chains=3, seed=71)\n",
    "    res=fit.extract()\n",
    "    print fit\n",
    "    return fit,res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def drawline(res,name,start=0):\n",
    "    _val=zip(*res[name])[start:-1]\n",
    "    _range=zip(*[ mquantiles(i) for i in _val])\n",
    "    fig, a = plt.subplots(1,sharex=True)\n",
    "    a.fill_between(range(len(_range[0])),_range[0],_range[2], color='blue',alpha=0.5)\n",
    "    a.plot(_range[1], lw=2, color='black')\n",
    "    a.plot([0]*len(_val))\n",
    "    plt.title(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_u=\"\"\"\n",
    "data{\n",
    "        int<lower=1> N;\n",
    "        real<lower=0,upper=100> pi[N];\n",
    "        real<lower=0,upper=100> u[N];\n",
    "}\n",
    "\n",
    "parameters{\n",
    "    real L; //lambda*phi    \n",
    "    real<lower=0> un; //natural unemployment rate\n",
    "    real<lower=-20,upper=20> beta[N];    \n",
    "    real u1;\n",
    "    real u0;\n",
    "    real<lower=0> su;\n",
    "    real<lower=0> s;\n",
    "}\n",
    "\n",
    "model{\n",
    "    real p[N-1];\n",
    "    \n",
    "    for(i in 2:(N-1))\n",
    "        u[i]~normal(u0+u1*u[i-1],su);\n",
    "  \n",
    "    for(i in 1:(N-1))\n",
    "        p[i]<-beta[i]*pi[i+1]-L*(u[i]-un);\n",
    "        \n",
    "    for(i in 1:(N-1))\n",
    "      pi[i]~normal(p[i],s);\n",
    "}         \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_70a72fe9d97728277d72433f4696a5d5.\n",
      "3 chains, each with iter=31000; warmup=1000; thin=10; \n",
      "post-warmup draws per chain=3000, total post-warmup draws=9000.\n",
      "\n",
      "            mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "L           0.08     0.1   1.64  -3.69  -0.51   0.04   0.65   3.74  269.0   1.01\n",
      "un         21.45    9.49  37.97   0.51    3.6   8.15  23.48 153.44   16.0   1.07\n",
      "beta[0]     0.37    0.58   7.48 -14.39  -4.36   0.46   5.25  14.79  169.0   1.01\n",
      "beta[1]     0.58    0.68   7.58 -14.25  -4.66   0.58   6.04  14.36  123.0   1.02\n",
      "beta[2]    -0.18    0.62   7.39  -13.9  -5.51  -0.47   4.81  14.62  144.0   1.02\n",
      "beta[3]    -0.13    0.67   7.34 -13.48  -5.49 6.7e-3   4.88  14.22  120.0   1.03\n",
      "beta[4]     0.36    0.51   7.04 -13.91  -4.14    0.2   4.91  14.27  188.0   1.01\n",
      "beta[5]      0.3    0.63   7.36 -13.64  -4.88   0.37   5.61  14.44  138.0   1.02\n",
      "beta[6]     0.09    0.56   7.08 -13.71  -4.52  -0.17   4.86  14.24  159.0   1.01\n",
      "beta[7]     0.31    0.62   7.18 -13.29  -4.66   0.28   5.37  13.95  135.0   1.02\n",
      "beta[8]     0.23    0.56   7.22 -13.78  -4.73   0.25   5.18  14.41  165.0   1.01\n",
      "beta[9]     0.03    0.58   7.18 -13.62  -4.87   0.07   4.93  14.21  155.0   1.02\n",
      "beta[10]    0.07    0.53   6.81 -13.32  -4.51   0.02   4.78  13.47  167.0   1.01\n",
      "beta[11]    0.76    0.71   7.95 -14.96  -4.66   0.71   6.74   15.6  126.0   1.02\n",
      "beta[12]   -0.08    0.55   6.75 -12.63  -5.06    0.2   4.59  13.08  150.0   1.02\n",
      "beta[13]    0.45    0.52   6.82 -13.16   -3.9   0.39   4.81  14.07  174.0   1.01\n",
      "beta[14]    0.33     0.7   7.48 -13.75  -4.89   0.64   5.66   14.2  113.0   1.03\n",
      "beta[15]     0.1    0.85   7.81 -16.11  -4.67    0.6   5.34  15.08   84.0   1.05\n",
      "beta[16]    0.13    0.54   6.94 -13.36  -4.68    0.2   4.92  13.74  164.0   1.01\n",
      "beta[17]   -0.06     0.6   6.72 -12.63   -4.7  -0.12   4.79  12.89  127.0   1.02\n",
      "beta[18]    0.22    0.54   7.02 -13.25  -4.82   0.17   4.78  14.71  170.0   1.01\n",
      "beta[19]   -0.23    0.69    6.9 -12.65  -4.99   -0.1   4.74  12.97   99.0   1.03\n",
      "beta[20]    0.64     0.7   7.13 -13.48  -4.19   0.41    5.5  14.14  104.0   1.03\n",
      "beta[21]    0.09    0.53   6.76  -12.5  -4.48  -0.19   4.68  13.67  160.0   1.01\n",
      "beta[22]    0.08    0.53   6.64 -12.67  -4.41   -0.1   4.48  13.07  155.0   1.02\n",
      "beta[23]    0.43    0.52    6.8 -13.11  -3.76   0.35   5.16  13.93  170.0   1.01\n",
      "beta[24]    0.15    0.58   6.93 -12.75  -4.78   0.21   5.02  13.74  143.0   1.02\n",
      "beta[25]   -0.05    0.62   7.03 -13.43  -4.76   0.14   4.85  13.15  128.0   1.02\n",
      "beta[26]   -0.06    0.62   6.97 -12.95  -5.15  -0.02   4.81  13.77  127.0   1.02\n",
      "beta[27]  6.3e-3     0.6   6.89 -12.56  -4.83  -0.14   4.75  13.32  130.0   1.02\n",
      "beta[28]    0.06    0.59   6.85 -12.96   -4.6   0.26   4.64  13.29  133.0   1.02\n",
      "beta[29]     0.2    0.49   6.57 -12.68  -3.96    0.1   4.58  13.54  179.0   1.01\n",
      "beta[30]   -0.23    0.72   7.03 -13.57  -5.17  -0.07   4.53  13.14   96.0   1.03\n",
      "beta[31]    0.06    0.59   6.81 -13.03  -4.24   0.03   4.73  13.07  135.0   1.02\n",
      "beta[32]    0.68    0.68   7.08 -12.62  -4.38   0.48   5.56  15.04  108.0   1.03\n",
      "beta[33]     0.3    0.56   6.89 -13.12  -4.76   0.59   5.14  13.33  152.0   1.02\n",
      "beta[34]    0.14    0.59    6.9 -12.97  -4.91  -0.02   4.74  14.39  135.0   1.02\n",
      "beta[35]    0.11    0.55   6.76 -12.87  -4.51   0.02   4.86  13.22  151.0   1.01\n",
      "beta[36]   -0.15    0.62   7.13 -13.16  -5.77  -0.06   4.88  13.59  133.0   1.02\n",
      "beta[37]    0.28    0.51   6.58 -12.79  -3.94   0.09   4.78  13.38  167.0   1.01\n",
      "beta[38]    0.53     0.5   6.64 -12.82  -3.64   0.46   5.07  13.57  177.0   1.01\n",
      "beta[39]    0.12    0.51   6.73  -13.7   -4.3   0.11   4.69  13.31  176.0   1.01\n",
      "beta[40]   -0.16    0.67   7.02 -13.84  -4.51   0.25   4.45  13.02  110.0   1.03\n",
      "beta[41]    0.77     0.5   6.64 -12.96  -3.21   1.01   5.15  13.18  175.0    1.0\n",
      "beta[42]    0.25    0.55   6.63 -12.57  -4.55   0.52   4.82  13.05  145.0   1.02\n",
      "beta[43]    0.19    0.59   6.92 -12.88  -4.56   0.32   4.83  13.57  139.0   1.02\n",
      "beta[44]    0.32    0.51   6.57 -13.23  -3.89   0.25   4.59  13.11  163.0   1.01\n",
      "beta[45]    0.19    0.51    6.5 -12.29  -4.26  -0.09   4.67  12.89  164.0   1.01\n",
      "beta[46]    0.17    0.52   6.53 -12.52  -3.99   0.35    4.6  13.08  158.0   1.01\n",
      "beta[47]    0.43    0.47   6.51 -12.53  -3.64   0.36   4.81  13.09  188.0   1.01\n",
      "beta[48]     0.9    0.49   6.39 -12.16  -3.01   0.81   5.44  13.34  169.0   1.01\n",
      "beta[49]    0.26     0.6   6.64 -12.45  -3.83   0.62   4.62  13.17  123.0   1.03\n",
      "beta[50]     0.3    0.65   6.71 -12.23  -4.22   0.64   5.17   12.7  106.0   1.03\n",
      "beta[51]    0.39    0.46   6.27 -12.32   -3.8   0.64   4.31  12.63  184.0   1.01\n",
      "beta[52]    0.08    0.52   6.32 -12.01  -4.06   0.29   4.25  12.42  149.0   1.02\n",
      "beta[53]    0.45    0.49   6.46 -12.14  -3.84   0.36   4.56  13.78  172.0   1.01\n",
      "beta[54]    0.34     0.5   6.41 -12.15  -3.93    0.6   4.61  12.43  163.0   1.01\n",
      "beta[55]    0.25    0.49   6.34 -11.99  -4.23   0.15    4.6  12.71  168.0   1.01\n",
      "beta[56]    0.47    0.45   6.39 -12.55  -3.54   0.53   4.48  13.21  203.0   1.01\n",
      "beta[57]   -0.29    0.66    6.8 -12.43  -5.37   -0.1   4.54  12.69  106.0   1.02\n",
      "beta[58]    0.73    0.46   6.33  -12.5  -2.92   0.76   4.86  12.97  186.0    1.0\n",
      "beta[59]    0.89    0.72   6.87 -11.95  -3.63   0.56   5.38  15.12   92.0   1.03\n",
      "beta[60]   -0.13    0.63    6.8 -12.38  -5.26   0.12   4.51  13.34  115.0   1.02\n",
      "beta[61]   -0.46    0.78   7.03 -13.98   -5.0  -0.02    4.4   12.7   82.0   1.04\n",
      "beta[62]   -0.05    0.56   6.45 -11.84  -4.87  -0.11   4.33  12.39  134.0   1.02\n",
      "beta[63]   -0.71    0.93   7.44 -17.47  -5.18-1.5e-3   4.37   12.5   64.0   1.05\n",
      "beta[64]    0.19    0.51   6.38 -11.78  -4.34   0.27   4.46  12.53  154.0   1.02\n",
      "beta[65]   -0.05    0.53   6.36 -12.07  -4.65  -0.07   4.37  12.41  145.0   1.02\n",
      "beta[66]    0.03    0.54   6.45 -11.96  -4.29   0.11   4.45  12.37  142.0   1.01\n",
      "beta[67]    0.21    0.52   6.35 -12.16  -3.94   0.11   4.36  12.86  148.0   1.01\n",
      "beta[68]    0.23    0.56    6.5 -11.92  -4.24   0.08   4.48  13.71  136.0   1.02\n",
      "beta[69]    0.25    0.55   6.66 -12.49  -4.27   0.26   4.54  13.79  148.0   1.02\n",
      "beta[70]    0.75    0.57   6.63 -12.17  -4.04    0.8   5.53  13.54  136.0   1.02\n",
      "beta[71]    0.72    0.57   6.62 -12.36  -3.68   0.67   5.46  12.93  134.0   1.02\n",
      "beta[72]    0.28    0.52   6.53 -12.35  -4.22   0.07   4.63  13.27  155.0   1.02\n",
      "beta[73]    0.47    0.76   7.13 -12.85  -4.11   0.83   5.67  13.09   88.0   1.04\n",
      "beta[74]    0.06    0.56   6.82 -12.84  -4.27   0.27   4.43  13.21  147.0   1.02\n",
      "beta[75]    0.59     0.6   6.96 -12.63  -4.25   0.46   5.85  13.36  134.0   1.02\n",
      "beta[76] -8.1e-3    0.69   6.99 -13.01   -4.5   0.38   4.58  13.48  102.0   1.03\n",
      "beta[77]    0.19    0.55   6.75 -12.76  -4.37   0.05   4.76  13.31  148.0   1.02\n",
      "beta[78]   -0.06     0.6   6.83 -12.74  -5.09  -0.12   4.88  13.26  128.0   1.02\n",
      "beta[79]    0.14    0.53   6.92 -13.72  -4.64   0.24   4.77  13.64  169.0   1.01\n",
      "beta[80]    -0.3     0.7   7.12 -13.06  -5.55  -0.17   4.65  13.15  104.0   1.03\n",
      "beta[81]   -0.11    0.62   6.95  -13.2  -4.61  -0.11   4.69  13.36  125.0   1.02\n",
      "beta[82]    0.59    0.54    6.7  -12.9  -4.09   0.72   5.23  13.59  156.0   1.01\n",
      "beta[83]   -0.25    0.78   7.47 -16.13  -4.84   0.06   4.69  13.67   92.0   1.03\n",
      "beta[84]    0.44    0.55   6.81  -13.0  -3.97   0.41   5.47  13.25  152.0   1.01\n",
      "beta[85]    0.13    0.56   6.75 -12.84   -4.7   0.28   4.78  13.21  145.0   1.02\n",
      "beta[86]    0.57    0.57   7.02 -12.95  -4.07   0.51   5.59  14.84  151.0   1.01\n",
      "beta[87]    0.24    0.54   6.66 -12.81  -4.29   0.47   4.56  13.33  153.0   1.02\n",
      "beta[88]    0.21    0.61   6.94 -12.92  -4.53    0.6   4.87  13.28  130.0   1.02\n",
      "beta[89]    0.42    0.53   6.53 -12.43   -4.1   0.57   4.96  13.12  149.0   1.02\n",
      "beta[90]    0.32    0.53   6.65 -13.05  -4.28   0.42   4.81   13.0  155.0   1.01\n",
      "beta[91]    0.28    0.44   6.27  -12.4  -3.45   0.26   4.28  13.06  199.0   1.01\n",
      "beta[92] -6.2e-3    0.55   6.36 -11.76  -3.94   0.05   4.28  12.42  133.0   1.02\n",
      "beta[93]    0.37     0.5   6.46 -12.46  -4.24   0.59   4.69  13.06  170.0   1.01\n",
      "beta[94]   -0.11    0.62   6.57 -12.59  -4.63   0.11   4.34  13.02  114.0   1.02\n",
      "beta[95]    0.17    0.65   6.73  -12.9  -4.04   0.74   4.55  12.48  106.0   1.03\n",
      "beta[96]    0.71    0.47   6.26 -11.97  -3.03   0.71    4.9  13.22  179.0   1.01\n",
      "beta[97]    0.12     0.5   6.49 -12.14  -4.42   0.07   4.44  13.08  166.0   1.01\n",
      "beta[98]    0.59    0.48    6.2 -11.83  -3.82   0.67    5.0  12.81  165.0   1.01\n",
      "beta[99]    0.23    0.48   6.24 -11.85   -4.2   0.08   4.49  12.49  166.0   1.01\n",
      "beta[100]   0.38    0.44   5.94  -11.3  -3.88   0.31   4.39  12.03  184.0   1.01\n",
      "beta[101]    0.6    0.53   6.27 -11.51  -3.76   0.79   5.16  12.26  141.0   1.02\n",
      "beta[102]   0.35    0.45   6.09 -11.88  -3.54   0.57   4.43  12.29  183.0   1.01\n",
      "beta[103]   0.44    0.48   6.07  -11.4  -3.76   0.69   4.77  11.99  162.0   1.01\n",
      "beta[104]  -0.04    0.57   6.42 -12.09   -4.6  -0.02   4.55  12.35  127.0   1.02\n",
      "beta[105]   0.34    0.45   6.14 -11.49  -3.83    0.1   4.39   12.7  184.0   1.01\n",
      "beta[106]   0.38    0.49   6.23  -11.5  -4.11   0.64    4.8  12.16  161.0   1.01\n",
      "beta[107]   0.06    0.61   6.41 -12.51  -3.76   0.43   4.51  12.01  110.0   1.03\n",
      "beta[108]   0.05    0.47    6.2 -11.85  -4.28   0.03   4.12  12.38  174.0   1.01\n",
      "beta[109]   0.17    0.47   6.17 -11.29  -4.59   0.08   4.45  12.46  172.0   1.01\n",
      "beta[110]   0.73    0.39   5.91 -10.84  -3.24   0.64   4.86  12.06  225.0   1.01\n",
      "beta[111]  -0.11    0.69   6.68  -13.9  -4.35   0.14   4.49  12.09   94.0   1.03\n",
      "beta[112]   0.62    0.49   6.27 -11.64  -4.03   0.89   5.23  12.33  165.0   1.02\n",
      "beta[113]   0.77    0.49    6.4 -11.41  -4.04   0.77   5.47  12.31  168.0   1.02\n",
      "beta[114]   0.03    0.57   6.54 -12.33  -4.42  -0.05   4.63  12.76  131.0   1.02\n",
      "beta[115]   0.63     0.5    6.2 -11.14   -4.0   0.73   5.32  12.19  156.0   1.02\n",
      "beta[116]   0.11    0.53   6.45  -11.3  -4.84-10.0e-3   4.68  12.88  146.0   1.02\n",
      "beta[117]    0.5    0.48    6.3 -11.46  -4.05   0.65   5.06  12.51  171.0   1.02\n",
      "beta[118]    0.3    0.47   6.38  -11.3  -4.57   0.09   4.81  12.58  182.0   1.01\n",
      "beta[119]   0.14    0.64   6.65 -13.38  -4.38   0.18   4.69  13.43  109.0   1.02\n",
      "beta[120]   0.77    0.35   6.09 -11.41  -3.08   0.76    4.7  12.52  305.0    1.0\n",
      "beta[121]   0.45    0.62   6.68 -12.15  -3.87    0.8   5.51  12.42  117.0   1.03\n",
      "beta[122]   0.45    0.52   6.72  -12.0  -4.19   0.79   5.22  13.09  166.0   1.02\n",
      "beta[123]   0.63    0.42   6.57 -12.38  -3.99   0.71   5.19  13.29  247.0   1.01\n",
      "beta[124]  -0.03    0.63   6.94  -13.2  -5.16   0.25   4.75  13.19  121.0   1.02\n",
      "beta[125]    0.2     0.5   6.75 -12.05  -4.99   0.14   4.85  14.07  181.0   1.01\n",
      "beta[126]   0.77    0.39   6.42  -11.9  -3.35   0.86   5.11  13.18  277.0   1.01\n",
      "beta[127]   0.92    0.41   6.41 -12.32  -3.21   0.78   5.51  13.25  240.0   1.01\n",
      "beta[128]   0.62    0.34   6.32 -11.98  -3.22   0.35   4.72  13.24  347.0    1.0\n",
      "beta[129]  -0.04    0.52   6.77 -12.31  -4.92  -0.01    4.5  13.65  169.0   1.01\n",
      "beta[130]   0.26    0.43   6.41 -11.92   -4.4   0.14    4.5  12.81  219.0   1.01\n",
      "beta[131]   0.45    0.43   6.39 -11.84  -4.07   0.26   4.64  13.22  222.0    1.0\n",
      "beta[132]   0.66    0.37   6.24 -12.25  -3.15   0.79   4.63  12.91  287.0    1.0\n",
      "beta[133]   0.21    0.56   6.53 -12.25  -3.87   0.14   4.72  12.67  135.0   1.02\n",
      "beta[134]   0.34    0.41   6.13 -11.26  -3.88   0.08   4.64  12.29  224.0   1.01\n",
      "beta[135]   0.36    0.41   6.06 -11.09  -3.93   0.19   4.59  12.41  220.0    1.0\n",
      "beta[136]   0.75    0.58   6.44 -11.92  -3.85   0.76   5.54  12.52  125.0   1.03\n",
      "beta[137]   0.68    0.61   6.54 -11.55  -4.07    0.6   5.36  12.68  116.0   1.03\n",
      "beta[138]   0.87    0.49   6.32 -11.04  -3.82   0.77   5.13  13.58  163.0   1.02\n",
      "beta[139]   0.69    0.43   6.19 -11.24  -3.56   0.66   5.11  12.53  210.0   1.01\n",
      "beta[140]   0.15    0.53   6.39 -11.93  -3.98   0.26   4.34  12.86  143.0   1.02\n",
      "beta[141]   0.66    0.48   6.31 -11.52  -3.68   0.78   5.14   13.0  173.0   1.01\n",
      "beta[142]   0.51    0.37   5.88 -11.36  -3.28   0.45   4.49  12.13  249.0    1.0\n",
      "beta[143]   0.34    0.52   6.28 -11.75  -3.62   0.35   4.59   12.3  148.0   1.02\n",
      "beta[144]   0.41    0.44   6.16 -11.56  -3.82   0.64   4.77  11.87  193.0   1.01\n",
      "beta[145]    0.7    0.49   6.31 -11.86  -3.66   0.62   5.36  12.35  169.0   1.01\n",
      "beta[146]   0.43    0.48   6.14 -11.08  -3.66   0.72   4.65  12.03  162.0   1.02\n",
      "beta[147]   0.16    0.48   6.07 -11.21   -4.1    0.1   4.32  11.95  157.0   1.01\n",
      "beta[148]   0.26    0.44   6.05 -11.62  -4.13   0.03   4.47  12.05  192.0   1.01\n",
      "beta[149]  -0.07    0.63    6.8 -12.33  -4.73   0.13   4.55  13.59  118.0   1.02\n",
      "beta[150]   0.57    0.44   6.23 -11.26   -3.5   0.74   4.47  13.22  201.0    1.0\n",
      "beta[151]   0.35    0.44   6.17 -11.46  -4.14   0.19   4.51  12.36  197.0   1.01\n",
      "beta[152]   0.69    0.49   6.31 -10.92  -3.97   0.76   5.23  12.51  168.0   1.02\n",
      "beta[153]   0.34    0.41   6.31 -11.48  -3.98    0.1   4.53  13.04  236.0    1.0\n",
      "beta[154]   0.35    0.44   6.56  -12.6  -4.35   0.11   4.98  13.23  223.0    1.0\n",
      "beta[155]  -0.01    0.52   6.56 -11.68  -4.93   0.01   4.66  12.81  158.0   1.01\n",
      "beta[156]   0.67     0.4    6.3 -11.36  -3.59   0.69   4.98  12.89  248.0   1.01\n",
      "beta[157]   0.53    0.37   6.34 -11.99  -3.85   0.48    4.7  12.87  295.0    1.0\n",
      "beta[158]   0.39    0.41   6.32 -11.49  -4.03   0.16   4.81  12.62  242.0    1.0\n",
      "beta[159]   0.12    0.47   6.47 -12.36  -4.62   0.15   4.77   12.7  188.0   1.01\n",
      "beta[160]   0.31    0.42   6.18 -11.57  -3.94   0.15    4.7  12.43  214.0   1.01\n",
      "beta[161]   0.31    0.41   6.16  -11.6   -3.9   0.12   4.53  12.63  222.0   1.01\n",
      "beta[162]   0.81    0.39   5.97 -11.32  -2.77   0.71   5.03  12.22  236.0   1.01\n",
      "beta[163]   -0.3    0.68   6.66 -14.59  -4.86 8.8e-3   4.25  11.86   95.0   1.02\n",
      "beta[164]   0.11    0.52    6.4 -11.73  -4.56   0.16   4.73  12.19  151.0   1.01\n",
      "beta[165]   0.65    0.42   6.07 -11.22  -3.77   0.59   5.02  12.07  204.0   1.01\n",
      "beta[166]   0.08     0.6   6.48 -13.08  -3.85   0.07   4.67  12.01  118.0   1.02\n",
      "beta[167]   0.08    0.45   6.06 -11.07   -4.3  -0.04   4.19  12.18  185.0   1.01\n",
      "beta[168]   0.42     0.4   6.07 -11.96  -3.73   0.39   4.53  12.15  227.0   1.01\n",
      "beta[169]   0.66    0.43   6.21 -11.31  -3.29   0.54   4.84   12.9  207.0   1.01\n",
      "beta[170]  -0.07    0.54   6.28  -11.6  -4.63    0.1   4.35  11.96  134.0   1.01\n",
      "beta[171]   0.33    0.42   5.96 -11.41  -3.73   0.14   4.39  12.16  204.0   1.01\n",
      "beta[172]   0.83    0.46   6.02 -11.16  -3.45   0.88   5.24  11.92  171.0   1.01\n",
      "beta[173]  -0.14    0.62   6.45 -11.74  -4.62   0.18   4.32   11.9  108.0   1.02\n",
      "beta[174]   0.27    0.51   6.35 -11.64   -4.2   0.53   4.49   12.6  154.0   1.02\n",
      "beta[175]-6.5e-3    0.55   6.33 -11.47  -4.65   0.02   4.44  12.36  132.0   1.02\n",
      "beta[176]   0.32    0.43   5.92 -11.91  -3.52   0.34   4.13  11.96  192.0   1.01\n",
      "beta[177]    1.5    0.86  11.55 -18.72  -8.51    2.6  12.25  18.77  181.0   1.01\n",
      "u1          0.99  5.3e-4 8.1e-3   0.97   0.98   0.99   0.99    1.0  232.0   1.01\n",
      "u0          0.06  2.1e-3   0.03-7.2e-4   0.04   0.06   0.08   0.11  175.0   1.01\n",
      "su          0.13  6.0e-4 7.2e-3   0.11   0.12   0.13   0.13   0.14  146.0   1.02\n",
      "s           3.62    0.16   1.65   0.81   2.39   3.54   4.76   6.84  111.0   1.03\n",
      "lp__      367.68    7.63 100.03 231.97 295.77 347.32 416.65 628.35  172.0   1.03\n",
      "\n",
      "Samples were drawn using NUTS(diag_e) at Fri Sep 23 03:33:19 2016.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "smodel_u = pystan.StanModel(model_code=model_u)\n",
    "fit_u = smodel_u.sampling(data, iter=31000, warmup=1000, thin=10, chains=3, seed=71)\n",
    "res_u=fit_u.extract()\n",
    "print fit_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"model_u\",\"wb\") as fpp:\n",
    "    pkl.dump(res_u,fpp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb2341fd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb20b4e350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb25074710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb20c5fd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(res_u['un'],bins=100)\n",
    "plt.title(\"natural unemployment rate\")\n",
    "plt.show()\n",
    "plt.hist(res_u['su'],bins=100)\n",
    "plt.title(\"sigma of unemployment rate\")\n",
    "plt.show()\n",
    "plt.hist(res_u['s'],bins=100)\n",
    "plt.title(\"sigma of price prediction\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(res_u['L'],bins=100)\n",
    "plt.title(\"lambda*phi\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb25c21c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zbjfwQFW9CXgQuHHIa0iSRmzY5J91znE1cFuzfBvwniGvIWkm7HBQ3wIZNvkX8KUkX03y\nK826nVW1ClBVK8DZQ15D0kx4GQf1LY6h6vyBy6vq2SQ/Btyf5CmOfWX4SpGkGTNU8q+qZ5vfzyX5\nQ+AyYDXJzqpaTbIE/OVGx+/du/fI8vLyMsvLy8OEowlYWjq/mdZB0iR0Oh06nc7Iz7vtQV5JTgNO\nqKoXk5wO3A/8G+AdwHer6qYkHwXOqqrd6xzvIK85tPnArv7laW9f1GvNViy+jydrVIO8hin57wT+\nIEk157m9qu5P8qfAXUmuA54Grhk2SEmzaseRBuCdO3exsnJwuuFoYE7voIG8trpndkqdsxNLWx/3\n+tM/aHyc3kETtTZ9s6RFYPKXpBYy+UsakbVBYCeeePqRZad9nk3D9vOXpEZvEBgcPrzWFrC66ojg\nWWTJX5JayOQvSS1k8pekFjL5a0O9+7Q6i6O0eEz+2tBa337790uLxuQvacx22OVzBpn8JY1Ztwvo\n6urKwH3/+6scB923f7+tHN9Wzu2jDa0/g+fszivTnmstRizHe/8f/dobbN+1/bZy/Lxxbh9ty2Yl\nIht5pXaw5N8ym5WINp+vfzFKnfN9rcWIxZL/9ljy10j1SvyS2sHkL8Apm6W2cWK3VtthaV9qKUv+\nrdabhdESv9Q2Jn9Jc8G++6Nl8m8JG3Q1W3Yck8g362bcP93I2v2ktV129WyJ/u5wdjOc92stXixV\nddxuxuttP7Zbp109t8IGX0lTNqqOB3Zg2AqrfRZM/1fn/vuoSrNr7faP6xs0qduBYSss+S+Y/v76\n/fdR7X6NluZRL6n3v4Yt5Q/Lkr+kObSVUv6xjcsy+S8Me/NIG1n7oLCX0BqT/xStNw/5djk9g6St\nsM5/inoJe3XVErukyRpbyT/JlUn+PMn/TvLRcV1nMexYt4dOb3mjbwbOvS9pu8aS/JOcAPwO8E7g\nJ4H3JvmJcVxrWjqdzgjPtlYnefjwS8cs99/+rv/DYbgbrHdGFv10dKYdwJA60w5gCJ1pBzCU0b53\n59e4Sv6XAQeq6umqegW4E7h6TNeaism+gNb/cBhOZ9igpqwz7QCG1Jl2AEPoTDuAIezg7W9/u71+\nGF/yPwd4pu/v7zTr5s4LL7zAJz/5SW6++WZuueUWXnrppWP22cqtER14JU3Ty8CeTXv9bPSeXqSb\nxdvgu4n77ruPG2644cjfF1xwAe9+97uB7j997UXULYn3N96ut92BV9IsWBskdsIJpzXfqF+7vN57\ner1OGv097VZXTyUJO3fuYmXl4LgfxFDGMrFbkrcBe6vqyubv3UBV1U19+9gvUZK2YRQTu40r+Z8I\nPAW8A3gWeAR4b1XtH/nFJElbNpZqn6r6YZKPAPfTbVf4tIlfkmbH1ObzlyRNz7j6+R93gFeSv5nk\n80m+nuQrSS7u23awWf9YkkfGEd9mhoz/zCSfS7I/yTeSvHUeYk/y481z/mjz+/tJfn2SsQ8Tf7Pt\nxuY5fyLJ7UlOmWz0Q8d/fZInm59pPPefTrKa5Inj7HNLkgNJHk9ySd/6qQ/q3Eb8b97KseO23ec/\nyblJHmxe+4O/dqpqpD90P1D+D7ALOBl4HPiJo/b5OPCvmuU3AQ/0bfsmcNao45pg/P8J+GCzfBLw\nunmJ/ajz/F/gvHl57ptjvgmc0vz9WeCX5yj+nwSeAHYAJ9KtMn3DhOP/aeAS4IkNtr8L+K/N8luB\nrwz6uGc5/kGOneX4gSXgkmb5DLrtrZs+/+Mo+Q8ywOti4EGAqnoKOD/JjzXbwnQnnNt2/EleB/xM\nVd3abHu1qn4wD7Eftc/PAn9RVc8wWcPE/wPg/wGnJzkJOI3uB9gkDRP/RcDDVfVyVf0QeAj4hcmF\nDlX1ZeD54+xyNfD7zb4PA2cm2cmMDOocIv5Bjh277cZfVStV9Xiz/kVgPwOMqxpHkh1kgNfXaV7Y\nSS4DXg+c22wr4EtJvprkV8cQ32aGif8C4K+S3NpUn/yHJD8ygZh7hn3ue34R+M9jivF4th1/VT0P\nfAL4NnAI+F5VPTD2iF9rmOf/z4CfSXJWktOAnwfOG3vEW7PR45uXQZ1Hx3mI2YxzI5vGn+R8ut8e\nHt7sZNMqYf8WcFaSR4FfAx4Dfthsu7yqLqX74v+1JD89pRiPZ6P4TwIuBf598xheAnZPLcr1He+5\nJ8nJwFXA56YT3qbWjT/JG4Ab6FY9/B3gjCTvm16YG1o3/qr6c+Am4EvAH3HU/2VGOUpxhiQ5A7gb\nuL75BnBc4+jqeYhuaabn3GbdEVX1AnBd7+8k36JbX0tVPdv8fi7JH9D9SvnlMcS5kWHiPx14pqr+\ntNl0NzDJxq+hnvvGu4CvVdVzY4xzI9uJ/5t04/954E+q6rvN+s8D/wC4Y8wx9xv2tX8rcGuz/t/x\n2lLeLDjEa7+N9B7fKWzyuGfERvHPiw3jb6o67wY+U1X3DHKycZT8vwpcmGRX09viWuDe/h2aHjEn\nN8u/CvyPqnoxyWnNpxdJTgeuoPt1eJK2HX9VrQLPJPnxZtd3APvmIfa+Xd7LdKp8YHvxP9TE/xTw\ntiSnJgnd537SY0uGev57bS9JXg/8Yyb7wXUkRDYu0d8L/DIcGcX/veY1v+njnqDtxD/IsZOy3fh/\nD9hXVb898JXG1Gp9Jd034wFgd7PuQ8A/bZbf1mzfT/fT6sxm/QV0ewo8BjzZO3bSP9uNv9n2d+m+\nGR4HPt+/bQ5iPw14Dvgb03jeRxD/vwS+QbfXzG3AyXMW/0N0CzuPActTiP0Ouo3kL9NtO/lgf+zN\nPr9Dt2fP14FLj/e45yz+Y46dg/jf3Ky7nG4VYS93Pgpcudn1HOQlSS3kPXwlqYVM/pLUQiZ/SWoh\nk78ktZDJX5JayOQvSS1k8pekFjL5S1IL/X9JRpPuYANMAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb2516c7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(res_u['u0'],bins=100)\n",
    "plt.title(\"u0\")\n",
    "plt.show()\n",
    "plt.hist(res_u['u1'],bins=100)\n",
    "plt.title(\"u1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbb24bd5b90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GBbbkGYZhghgWeYZhmCCGRZ5hGCaIYZFnGIYJYljkGYZhghgWeYZhmCCGRZ5h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\nRldXPTZv/qu2bU3NPs2ST0u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1qKnZo7lpKlYdGhqORYseRltbJfbseRHvvXczbr99DcrK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\nt1gslmXM/wFnP9wVWB+QYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb250c73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "beta_val=zip(*res_u['beta'])[:-1]\n",
    "beta_range=zip(*[ mquantiles(i) for i in beta_val])\n",
    "fig, a = plt.subplots(1,sharex=True)\n",
    "a.fill_between(range(len(beta_range[0])),beta_range[0],beta_range[2], color='blue',alpha=0.5)\n",
    "a.plot(beta_range[1], lw=2, color='black')\n",
    "a.plot([0]*len(beta_val))\n",
    "plt.title(\"beta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ub=\"\"\"\n",
    "data{\n",
    "        int<lower=1> N;\n",
    "        real<lower=0,upper=100> pi[N];\n",
    "        real<lower=0,upper=100> u[N];\n",
    "}\n",
    "\n",
    "parameters{\n",
    "    real L; //lambda*phi    \n",
    "    real<lower=0> un; //natural unemployment rate\n",
    "    real<lower=-20,upper=20> beta[N];    \n",
    "    real u1;\n",
    "    real u0;\n",
    "    real b1;\n",
    "    real b0;\n",
    "    real<lower=0> sb;\n",
    "    real<lower=0> su;\n",
    "    real<lower=0> s;\n",
    "}\n",
    "\n",
    "model{\n",
    "    real p[N-1];\n",
    "    \n",
    "    for(i in 2:(N-1)){\n",
    "        u[i]~normal(u0+u1*u[i-1],su);\n",
    "        beta[i]~normal(b0+b1*beta[i-1],sb);\n",
    "    }\n",
    "    for(i in 1:(N-1))\n",
    "        p[i]<-beta[i]*pi[i+1]-L*(u[i]-un);\n",
    "        \n",
    "    for(i in 1:(N-1))\n",
    "      pi[i]~normal(p[i],s);\n",
    "}         \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_a63fc9f1148778aef21d129315cdc88f.\n",
      "3 chains, each with iter=31000; warmup=1000; thin=10; \n",
      "post-warmup draws per chain=3000, total post-warmup draws=9000.\n",
      "\n",
      "            mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "L        -9.6e-3    0.02   0.08-3.5e-3 6.1e-4 3.4e-3 5.2e-3 7.5e-3   27.0   1.08\n",
      "un         48.38   34.58  48.91   0.39   1.29  32.62 104.02 136.15    2.0   5.56\n",
      "beta[0]     0.78    0.21    0.3   0.27   0.44   0.92   1.04   1.05    2.0   4.04\n",
      "beta[1]     0.73    0.21    0.3   0.22   0.38   0.92   0.96   0.97    2.0   3.73\n",
      "beta[2]     0.76    0.21   0.29   0.26   0.42   0.91   1.02   1.02    2.0   3.86\n",
      "beta[3]     0.73    0.21    0.3   0.24    0.4   0.91   0.98   0.99    2.0   3.48\n",
      "beta[4]     0.74    0.21   0.29   0.25   0.41    0.9    1.0   1.01    2.0   3.41\n",
      "beta[5]     0.75    0.21    0.3   0.25   0.42    0.9   1.01   1.02    2.0   3.46\n",
      "beta[6]     0.73    0.21    0.3   0.24    0.4    0.9   0.98   0.99    2.0   3.29\n",
      "beta[7]     0.74    0.21   0.29   0.25   0.41    0.9    1.0    1.0    2.0   3.43\n",
      "beta[8]     0.74    0.21   0.29   0.25   0.41    0.9   0.99    1.0    2.0   3.88\n",
      "beta[9]     0.74    0.21   0.29   0.25   0.42    0.9    1.0   1.01    2.0   3.52\n",
      "beta[10]    0.73     0.2   0.29   0.24    0.4    0.9   0.96   0.97    2.0    3.7\n",
      "beta[11]    0.79    0.21    0.3   0.29   0.49    0.9    1.1    1.1    2.0   4.65\n",
      "beta[12]    0.67     0.2   0.29   0.19   0.34   0.86    0.9   0.92    2.0   3.27\n",
      "beta[13]    0.78     0.2   0.28    0.3   0.48    0.9   1.06   1.07    2.0   4.02\n",
      "beta[14]    0.76    0.21    0.3   0.25   0.44    0.9   1.05   1.05    2.0   3.65\n",
      "beta[15]    0.74    0.21    0.3   0.23    0.4    0.9    1.0   1.01    2.0   3.01\n",
      "beta[16]    0.73    0.22   0.31   0.23   0.41    0.9    1.0   1.01    2.0   2.72\n",
      "beta[17]    0.71    0.21    0.3   0.21   0.38    0.9   0.94   0.95    2.0    2.6\n",
      "beta[18]    0.77    0.22   0.31   0.27   0.46    0.9   1.08   1.08    2.0   2.69\n",
      "beta[19]    0.71    0.22   0.32   0.21   0.38    0.9   0.96   0.97    2.0   2.29\n",
      "beta[20]    0.74    0.22   0.31   0.24   0.42    0.9   1.02   1.02    2.0   2.42\n",
      "beta[21]    0.73    0.22   0.32   0.23   0.41    0.9   1.01   1.01    2.0   2.38\n",
      "beta[22]    0.72    0.22   0.31   0.22   0.39    0.9   0.98   0.98    2.0    2.4\n",
      "beta[23]    0.74    0.22   0.31   0.24   0.42    0.9   1.02   1.03    2.0   2.55\n",
      "beta[24]    0.73    0.22   0.31   0.22    0.4    0.9   0.99    1.0    2.0   2.45\n",
      "beta[25]    0.73    0.22   0.31   0.23   0.41    0.9    1.0   1.01    2.0   2.39\n",
      "beta[26]    0.73    0.22   0.31   0.22    0.4    0.9   0.99    1.0    2.0   2.37\n",
      "beta[27]    0.73    0.22   0.31   0.23    0.4    0.9    1.0    1.0    2.0   2.41\n",
      "beta[28]    0.73    0.23   0.32   0.23   0.41    0.9   1.01   1.02    2.0   2.27\n",
      "beta[29]    0.73    0.23   0.32   0.23    0.4    0.9   1.01   1.01    2.0   2.17\n",
      "beta[30]    0.72    0.23   0.32   0.22   0.39    0.9   0.98   0.99    2.0   2.12\n",
      "beta[31]    0.73    0.23   0.32   0.23   0.41    0.9   1.01   1.01    2.0   2.23\n",
      "beta[32]    0.73    0.22   0.32   0.23    0.4    0.9    1.0   1.01    2.0   2.32\n",
      "beta[33]    0.72    0.22   0.31   0.22    0.4    0.9   0.99   0.99    2.0   2.37\n",
      "beta[34]    0.74    0.23   0.32   0.23   0.42    0.9   1.02   1.03    2.0   2.31\n",
      "beta[35]    0.72    0.22   0.32   0.21   0.39    0.9   0.98   0.98    2.0   2.32\n",
      "beta[36]    0.74    0.22   0.31   0.24   0.41    0.9   1.01   1.02    2.0   2.55\n",
      "beta[37]    0.72    0.22   0.31   0.22   0.39    0.9   0.98   0.99    2.0   2.44\n",
      "beta[38]    0.74    0.22   0.31   0.23   0.41    0.9   1.01   1.02    2.0    2.4\n",
      "beta[39]    0.73    0.23   0.32   0.22    0.4    0.9    1.0   1.01    2.0   2.26\n",
      "beta[40]    0.72    0.23   0.32   0.22    0.4    0.9    1.0    1.0    2.0   2.22\n",
      "beta[41]    0.73    0.23   0.32   0.22    0.4    0.9    1.0    1.0    2.0   2.16\n",
      "beta[42]    0.73    0.23   0.32   0.22    0.4    0.9    1.0    1.0    2.0   2.25\n",
      "beta[43]    0.74    0.23   0.32   0.23   0.41    0.9   1.02   1.02    2.0   2.24\n",
      "beta[44]    0.72    0.23   0.32   0.21   0.39    0.9   0.98   0.99    2.0   2.17\n",
      "beta[45]    0.72    0.23   0.32   0.23    0.4    0.9    1.0    1.0    2.0   2.12\n",
      "beta[46]    0.74    0.23   0.33   0.23   0.41    0.9   1.02   1.03    2.0   2.11\n",
      "beta[47]    0.72    0.23   0.33   0.22   0.39    0.9   0.99    1.0    2.0   2.01\n",
      "beta[48]    0.72    0.24   0.34   0.23    0.4    0.9   1.01   1.01    2.0    1.9\n",
      "beta[49]    0.72    0.24   0.34   0.22    0.4    0.9    1.0    1.0    2.0   1.89\n",
      "beta[50]    0.72    0.24   0.34   0.22    0.4    0.9   0.99    1.0    2.0   1.87\n",
      "beta[51]    0.72    0.24   0.33   0.23    0.4    0.9    1.0    1.0    2.0   1.93\n",
      "beta[52]    0.72    0.24   0.34   0.23    0.4    0.9    1.0   1.01    2.0   1.89\n",
      "beta[53]    0.73    0.24   0.34   0.23   0.41    0.9   1.01   1.02    2.0   1.87\n",
      "beta[54]    0.71    0.24   0.34   0.21   0.39    0.9   0.98   0.99    2.0   1.85\n",
      "beta[55]    0.73    0.24   0.34   0.23   0.41    0.9   1.01   1.02    2.0   1.91\n",
      "beta[56]    0.72    0.24   0.33   0.22   0.39    0.9   0.99    1.0    2.0   1.95\n",
      "beta[57]    0.72    0.24   0.33   0.22    0.4    0.9    1.0    1.0    2.0   1.96\n",
      "beta[58]    0.73    0.24   0.34   0.23   0.41    0.9   1.01   1.02    2.0   1.95\n",
      "beta[59]    0.71    0.24   0.34   0.22   0.39    0.9   0.99   0.99    2.0   1.84\n",
      "beta[60]    0.73    0.24   0.34   0.23   0.41    0.9   1.01   1.02    2.0    1.9\n",
      "beta[61]    0.71    0.24   0.34   0.22   0.39    0.9   0.99   0.99    2.0   1.84\n",
      "beta[62]    0.72    0.25   0.35   0.23    0.4    0.9   1.01   1.01    2.0   1.81\n",
      "beta[63]    0.72    0.24   0.35   0.22   0.39    0.9   0.99    1.0    2.0   1.82\n",
      "beta[64]    0.72    0.25   0.35   0.23    0.4    0.9   1.01   1.01    2.0   1.76\n",
      "beta[65]    0.71    0.25   0.35   0.22    0.4    0.9   0.99    1.0    2.0   1.73\n",
      "beta[66]    0.72    0.24   0.34   0.22    0.4    0.9   0.99    1.0    2.0   1.83\n",
      "beta[67]    0.72    0.24   0.34   0.23   0.41    0.9   1.01   1.01    2.0   1.88\n",
      "beta[68]    0.72    0.24   0.34   0.22    0.4    0.9    1.0    1.0    2.0   1.89\n",
      "beta[69]    0.72    0.23   0.33   0.22    0.4    0.9    1.0    1.0    2.0   2.01\n",
      "beta[70]    0.72    0.24   0.33   0.23    0.4    0.9    1.0   1.01    2.0   1.98\n",
      "beta[71]    0.72    0.23   0.33   0.22    0.4    0.9    1.0    1.0    2.0   2.06\n",
      "beta[72]    0.72    0.23   0.33   0.22    0.4    0.9    1.0    1.0    2.0   2.11\n",
      "beta[73]    0.73    0.23   0.32   0.23    0.4    0.9    1.0   1.01    2.0   2.14\n",
      "beta[74]    0.72    0.23   0.32   0.22    0.4    0.9   0.99    1.0    2.0   2.21\n",
      "beta[75]    0.73    0.23   0.32   0.23   0.41    0.9   1.01   1.01    2.0   2.26\n",
      "beta[76]    0.73    0.23   0.32   0.22    0.4    0.9   0.99    1.0    2.0   2.28\n",
      "beta[77]    0.73    0.23   0.32   0.23   0.41    0.9   1.01   1.01    2.0   2.29\n",
      "beta[78]    0.72    0.22   0.32   0.21   0.39    0.9   0.98   0.99    2.0   2.32\n",
      "beta[79]    0.74    0.22   0.32   0.23   0.41    0.9   1.02   1.03    2.0   2.37\n",
      "beta[80]    0.72    0.23   0.32   0.21   0.39    0.9   0.98   0.99    2.0   2.29\n",
      "beta[81]    0.73    0.22   0.32   0.23    0.4    0.9    1.0   1.01    2.0   2.31\n",
      "beta[82]    0.73    0.22   0.32   0.22    0.4    0.9    1.0    1.0    2.0   2.31\n",
      "beta[83]    0.73    0.23   0.32   0.23   0.41    0.9   1.01   1.02    2.0   2.35\n",
      "beta[84]    0.72    0.22   0.32   0.22   0.39    0.9   0.99    1.0    2.0   2.32\n",
      "beta[85]    0.72    0.22   0.32   0.22    0.4    0.9   0.99    1.0    2.0   2.29\n",
      "beta[86]    0.73    0.23   0.32   0.23    0.4    0.9   1.01   1.02    2.0   2.26\n",
      "beta[87]    0.73    0.23   0.33   0.22    0.4    0.9   1.01   1.01    2.0   2.17\n",
      "beta[88]    0.71    0.23   0.33   0.21   0.39    0.9   0.98   0.99    2.0   2.11\n",
      "beta[89]    0.72    0.23   0.33   0.22    0.4    0.9   0.99    1.0    2.0   2.03\n",
      "beta[90]    0.73    0.24   0.33   0.23   0.41    0.9   1.01   1.02    2.0   1.99\n",
      "beta[91]    0.72    0.24   0.34   0.22    0.4    0.9    1.0    1.0    2.0   1.85\n",
      "beta[92]    0.71    0.24   0.35   0.22   0.39    0.9   0.98   0.99    2.0   1.79\n",
      "beta[93]    0.74    0.25   0.35   0.24   0.42    0.9   1.05   1.05    2.0   1.85\n",
      "beta[94]    0.69    0.25   0.35    0.2   0.37    0.9   0.95   0.96    2.0   1.72\n",
      "beta[95]    0.73    0.25   0.35   0.24   0.42    0.9   1.02   1.03    2.0   1.78\n",
      "beta[96]    0.71    0.25   0.36   0.22   0.39    0.9   0.99    1.0    2.0   1.73\n",
      "beta[97]    0.73    0.21   0.36   0.23   0.41    0.9   1.02   1.03    3.0   1.72\n",
      "beta[98]     0.7    0.21   0.36   0.21   0.38    0.9   0.97   0.98    3.0   1.66\n",
      "beta[99]    0.72    0.21   0.37   0.24   0.41    0.9   1.02   1.02    3.0   1.62\n",
      "beta[100]   0.71    0.21   0.37   0.22   0.39    0.9   0.98   0.99    3.0    1.6\n",
      "beta[101]   0.72    0.22   0.37   0.23   0.41    0.9   1.01   1.02    3.0    1.6\n",
      "beta[102]   0.71    0.21   0.37   0.22    0.4    0.9   0.99    1.0    3.0   1.61\n",
      "beta[103]   0.71    0.21   0.37   0.22    0.4    0.9   0.99    1.0    3.0   1.61\n",
      "beta[104]   0.72    0.22   0.37   0.23   0.41    0.9   1.02   1.03    3.0   1.63\n",
      "beta[105]   0.71    0.22   0.37   0.22   0.39    0.9   0.99   0.99    3.0   1.61\n",
      "beta[106]   0.71    0.22   0.38   0.22    0.4    0.9    1.0   1.01    3.0   1.58\n",
      "beta[107]   0.71    0.22   0.38   0.21    0.4    0.9   0.99    1.0    3.0   1.57\n",
      "beta[108]   0.72    0.23   0.39   0.22   0.41    0.9   1.02   1.02    3.0   1.53\n",
      "beta[109]    0.7    0.21   0.41   0.21   0.39    0.9   0.99    1.0    4.0   1.43\n",
      "beta[110]    0.7    0.21   0.42   0.22    0.4    0.9   0.99    1.0    4.0    1.4\n",
      "beta[111]    0.7    0.22   0.43   0.22   0.41    0.9   1.01   1.02    4.0   1.39\n",
      "beta[112]    0.7    0.22   0.45   0.22    0.4    0.9    1.0   1.01    4.0   1.36\n",
      "beta[113]   0.69    0.23   0.45   0.21    0.4    0.9   0.99   0.99    4.0   1.34\n",
      "beta[114]    0.7    0.23   0.45   0.23   0.41    0.9   1.01   1.01    4.0   1.35\n",
      "beta[115]    0.7    0.22   0.45   0.22    0.4    0.9    1.0    1.0    4.0   1.35\n",
      "beta[116]    0.7    0.23   0.46   0.23   0.41    0.9    1.0   1.01    4.0   1.33\n",
      "beta[117]   0.69    0.23   0.45   0.22    0.4    0.9   0.99    1.0    4.0   1.34\n",
      "beta[118]    0.7    0.23   0.45   0.22   0.41    0.9   1.01   1.01    4.0   1.35\n",
      "beta[119]   0.69    0.23   0.45   0.22   0.39    0.9   0.99   0.99    4.0   1.34\n",
      "beta[120]    0.7    0.23   0.46   0.23   0.42    0.9   1.02   1.03    4.0   1.34\n",
      "beta[121]   0.68    0.21   0.46   0.21   0.39    0.9   0.98   0.99    5.0   1.32\n",
      "beta[122]    0.7    0.22   0.48   0.23   0.41    0.9   1.02   1.03    5.0    1.3\n",
      "beta[123]   0.68    0.22    0.5   0.21   0.39    0.9   0.98   0.99    5.0   1.27\n",
      "beta[124]    0.7    0.22   0.49   0.22   0.41    0.9   1.01   1.02    5.0   1.29\n",
      "beta[125]   0.68    0.22    0.5   0.21    0.4    0.9   0.99    1.0    5.0   1.27\n",
      "beta[126]   0.68    0.22    0.5   0.22    0.4    0.9   0.99    1.0    5.0   1.27\n",
      "beta[127]   0.69    0.22   0.49   0.22    0.4    0.9   0.99    1.0    5.0   1.28\n",
      "beta[128]    0.7    0.22   0.49   0.23   0.41    0.9   1.01   1.02    5.0   1.28\n",
      "beta[129]   0.69    0.23    0.5   0.22   0.41    0.9   1.01   1.02    5.0   1.27\n",
      "beta[130]   0.68    0.21   0.48   0.21   0.39    0.9   0.98   0.99    5.0   1.29\n",
      "beta[131]    0.7    0.21   0.47   0.23   0.41    0.9   1.01   1.02    5.0   1.31\n",
      "beta[132]   0.69    0.21   0.46   0.22    0.4    0.9   0.99    1.0    5.0   1.32\n",
      "beta[133]    0.7    0.23   0.45   0.22   0.41    0.9    1.0   1.01    4.0   1.34\n",
      "beta[134]   0.69    0.23   0.45   0.22    0.4    0.9   0.99    1.0    4.0   1.34\n",
      "beta[135]    0.7    0.22   0.44   0.23   0.41    0.9   1.01   1.02    4.0   1.37\n",
      "beta[136]   0.69    0.22   0.44   0.22    0.4    0.9   0.99    1.0    4.0   1.37\n",
      "beta[137]    0.7    0.22   0.43   0.22   0.41    0.9    1.0   1.01    4.0   1.38\n",
      "beta[138]    0.7    0.21   0.42   0.22    0.4    0.9   0.99    1.0    4.0    1.4\n",
      "beta[139]   0.71    0.22   0.43   0.23   0.41    0.9   1.01   1.02    4.0   1.38\n",
      "beta[140]    0.7    0.21   0.42   0.22    0.4    0.9   0.99    1.0    4.0   1.42\n",
      "beta[141]   0.71    0.21   0.42   0.22   0.41    0.9   1.01   1.02    4.0   1.42\n",
      "beta[142]    0.7    0.21   0.41   0.21   0.39    0.9   0.98   0.99    4.0   1.43\n",
      "beta[143]   0.72    0.24   0.41   0.23   0.41    0.9   1.02   1.03    3.0   1.45\n",
      "beta[144]   0.69    0.24   0.41    0.2   0.39    0.9   0.97   0.98    3.0   1.45\n",
      "beta[145]   0.72    0.23    0.4   0.23   0.42    0.9   1.02   1.03    3.0    1.5\n",
      "beta[146]    0.7    0.23   0.39   0.21   0.39    0.9   0.98   0.99    3.0    1.5\n",
      "beta[147]   0.71    0.23    0.4   0.23    0.4    0.9    1.0   1.01    3.0   1.49\n",
      "beta[148]   0.71    0.23    0.4   0.22    0.4    0.9    1.0   1.01    3.0   1.48\n",
      "beta[149]   0.71    0.24   0.41   0.22   0.41    0.9   1.01   1.02    3.0   1.46\n",
      "beta[150]    0.7    0.24   0.41   0.21   0.39    0.9   0.98   0.99    3.0   1.45\n",
      "beta[151]   0.71    0.24   0.42   0.23   0.41    0.9   1.02   1.03    3.0   1.44\n",
      "beta[152]   0.69    0.22   0.45   0.21   0.39    0.9   0.98   0.99    4.0   1.35\n",
      "beta[153]   0.68    0.21   0.48   0.21    0.4    0.9   0.99   0.99    5.0    1.3\n",
      "beta[154]   0.69    0.22    0.5   0.22   0.41    0.9    1.0   1.01    5.0   1.27\n",
      "beta[155]   0.69    0.21   0.48   0.22    0.4    0.9   0.99    1.0    5.0   1.29\n",
      "beta[156]    0.7    0.21   0.47   0.23   0.41    0.9    1.0   1.01    5.0   1.31\n",
      "beta[157]   0.69    0.21   0.48   0.22   0.41    0.9   1.01   1.01    5.0    1.3\n",
      "beta[158]   0.69    0.21   0.48   0.22    0.4    0.9    1.0   1.01    5.0    1.3\n",
      "beta[159]   0.69    0.21   0.47   0.22    0.4    0.9   0.99    1.0    5.0   1.31\n",
      "beta[160]    0.7    0.23   0.45   0.23   0.41    0.9   1.01   1.02    4.0   1.34\n",
      "beta[161]   0.69    0.22   0.45   0.22    0.4    0.9   0.99    1.0    4.0   1.35\n",
      "beta[162]    0.7    0.22   0.44   0.22    0.4    0.9    1.0   1.01    4.0   1.37\n",
      "beta[163]    0.7    0.22   0.44   0.22    0.4    0.9    1.0   1.01    4.0   1.38\n",
      "beta[164]    0.7    0.22   0.44   0.22   0.41    0.9   1.01   1.01    4.0   1.37\n",
      "beta[165]    0.7    0.22   0.43   0.22    0.4    0.9   0.99    1.0    4.0   1.38\n",
      "beta[166]    0.7    0.21   0.42   0.22    0.4    0.9    1.0   1.01    4.0   1.41\n",
      "beta[167]    0.7    0.21   0.42   0.22    0.4    0.9   0.99    1.0    4.0   1.42\n",
      "beta[168]   0.71    0.21   0.42   0.23   0.41    0.9    1.0   1.01    4.0   1.42\n",
      "beta[169]   0.71    0.24   0.41   0.22    0.4    0.9   1.01   1.02    3.0   1.45\n",
      "beta[170]    0.7    0.23   0.41   0.21   0.39    0.9   0.98   0.99    3.0   1.45\n",
      "beta[171]   0.71    0.23    0.4   0.23   0.41    0.9   1.01   1.01    3.0   1.48\n",
      "beta[172]    0.7    0.22   0.39   0.22    0.4    0.9   0.99    1.0    3.0   1.53\n",
      "beta[173]   0.72    0.22   0.39   0.23   0.41    0.9   1.01   1.02    3.0   1.54\n",
      "beta[174]   0.71    0.22   0.39   0.22    0.4    0.9    1.0    1.0    3.0   1.54\n",
      "beta[175]   0.71    0.22   0.38   0.22    0.4    0.9    1.0   1.01    3.0   1.57\n",
      "beta[176]   0.71    0.22   0.38   0.22    0.4    0.9   0.99    1.0    3.0   1.56\n",
      "beta[177]  -2.88     5.9  10.22 -17.64 -12.87  -4.18   5.65  15.83    3.0   1.58\n",
      "u1          0.99  5.8e-4 6.8e-3   0.97   0.98   0.98   0.99    1.0  139.0   1.03\n",
      "u0          0.06  5.4e-3   0.02 4.7e-3   0.04   0.06   0.07    0.1   21.0   1.08\n",
      "b1         -0.13    0.45   0.63  -0.74  -0.64   -0.5   0.71    0.8    2.0   4.93\n",
      "b0           0.8    0.43   0.61   0.19   0.26   0.49   1.64   1.74    2.0    4.9\n",
      "sb          0.01  2.5e-3   0.01 2.9e-3 3.0e-3   0.01   0.02   0.02   21.0    1.1\n",
      "su          0.13  1.3e-3 6.5e-3   0.12   0.12   0.13   0.13   0.14   27.0   1.18\n",
      "s           0.01  6.1e-3 8.6e-3 1.1e-3 3.2e-3 8.4e-3   0.02   0.02    2.0   2.43\n",
      "lp__      2206.2   60.44 104.69 1980.5 2142.7 2200.6 2241.1 2426.3    3.0   1.78\n",
      "\n",
      "Samples were drawn using NUTS(diag_e) at Sat Sep 24 02:30:46 2016.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "smodel_ub = pystan.StanModel(model_code=model_ub)\n",
    "fit_ub = smodel_ub.sampling(data, iter=31000, warmup=1000, thin=10, chains=3, seed=71)\n",
    "res_ub=fit_ub.extract()\n",
    "print fit_ub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## beta AR(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ub2=\"\"\"\n",
    "data{\n",
    "        int<lower=1> N;\n",
    "        real<lower=0,upper=100> pi[N];\n",
    "        real<lower=0,upper=100> u[N];\n",
    "}\n",
    "\n",
    "parameters{\n",
    "    real L; //lambda*phi    \n",
    "    real<lower=0> un; //natural unemployment rate\n",
    "    real<lower=-20,upper=20> beta[N];    \n",
    "    real u1;\n",
    "    real u0;\n",
    "    real b[3];\n",
    "    real<lower=0> sb;\n",
    "    real<lower=0> su;\n",
    "    real<lower=0> s;\n",
    "}\n",
    "\n",
    "model{\n",
    "    real p[N-1];\n",
    "    \n",
    "    for(i in 2:(N-1))\n",
    "        u[i]~normal(u0+u1*u[i-1],su);\n",
    "\n",
    "    for(i in 3:(N-1))\n",
    "        beta[i]~normal(b[1]+b[2]*beta[i-1]+b[3]*beta[i-2],sb);\n",
    "\n",
    "    for(i in 1:(N-1))\n",
    "        p[i]<-beta[i]*pi[i+1]-L*(u[i]-un);\n",
    "        \n",
    "    for(i in 1:(N-1))\n",
    "        pi[i]~normal(p[i],s);\n",
    "}         \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_f8db573e0ff350f2f4332e734b0fc421.\n",
      "3 chains, each with iter=31000; warmup=1000; thin=10; \n",
      "post-warmup draws per chain=3000, total post-warmup draws=9000.\n",
      "\n",
      "            mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "L        -4.7e-3  8.0e-3   0.06-5.1e-3 2.3e-4 7.4e-4 4.9e-3   0.01   48.0   1.04\n",
      "un          8.19    8.43  11.92   0.01   0.04   0.09   22.0  29.94    2.0   5.25\n",
      "beta[0]     0.99    0.05   0.08   0.89   0.91   1.04   1.04   1.05    2.0   1.77\n",
      "beta[1]     0.92    0.05   0.07   0.83   0.85   0.96   0.97   0.97    2.0   1.69\n",
      "beta[2]     0.97    0.04   0.07   0.86    0.9   1.02   1.02   1.03    3.0   1.57\n",
      "beta[3]     0.94    0.04   0.07   0.84   0.86   0.98   0.98   0.99    3.0   1.52\n",
      "beta[4]     0.95    0.04   0.08   0.86   0.88    1.0    1.0   1.01    3.0   1.47\n",
      "beta[5]     0.96    0.06   0.09   0.86   0.87   1.01   1.02   1.02    2.0    1.6\n",
      "beta[6]     0.94    0.04   0.08   0.85   0.87   0.98   0.98   0.99    3.0   1.44\n",
      "beta[7]     0.95    0.04   0.07   0.86   0.89    1.0    1.0   1.01    3.0   1.42\n",
      "beta[8]     0.94    0.05   0.08   0.85   0.86   0.99    1.0    1.0    2.0   1.72\n",
      "beta[9]     0.96    0.04   0.07   0.88    0.9    1.0    1.0   1.01    3.0   1.41\n",
      "beta[10]    0.92    0.04   0.07   0.83   0.86   0.96   0.97   0.97    3.0   1.58\n",
      "beta[11]    1.03    0.07    0.1    0.9   0.92    1.1    1.1    1.1    2.0    2.0\n",
      "beta[12]    0.85    0.01   0.04   0.81   0.84   0.86   0.87   0.89   15.0   1.07\n",
      "beta[13]    1.01    0.06   0.08    0.9   0.92   1.06   1.06   1.07    2.0   1.71\n",
      "beta[14]    0.98    0.07    0.1   0.85   0.87   1.05   1.05   1.06    2.0   1.83\n",
      "beta[15]    0.95    0.04   0.08   0.87   0.89    1.0    1.0   1.01    4.0   1.34\n",
      "beta[16]    0.95    0.05   0.09   0.85   0.87    1.0    1.0   1.01    4.0    1.3\n",
      "beta[17]    0.91    0.03   0.08   0.84   0.86   0.94   0.94   0.95    8.0   1.15\n",
      "beta[18]    1.01    0.07   0.12   0.88    0.9   1.08   1.08   1.08    3.0    1.4\n",
      "beta[19]    0.92    0.03    0.1   0.84   0.87   0.96   0.96   0.97    9.0   1.13\n",
      "beta[20]    0.96    0.05   0.11   0.86   0.89   1.02   1.02   1.02    5.0   1.22\n",
      "beta[21]    0.95    0.05   0.11   0.86   0.89   1.01   1.01   1.02    5.0   1.21\n",
      "beta[22]    0.93    0.04    0.1   0.85   0.88   0.98   0.98   0.99    7.0   1.16\n",
      "beta[23]    0.97    0.05   0.11   0.87   0.89   1.02   1.03   1.03    4.0   1.27\n",
      "beta[24]    0.94    0.04    0.1   0.85   0.88   0.99   0.99    1.0    5.0   1.21\n",
      "beta[25]    0.95    0.04    0.1   0.86   0.88    1.0   1.01   1.01    6.0   1.19\n",
      "beta[26]    0.94    0.04    0.1   0.86   0.88   0.99    1.0    1.0    6.0   1.18\n",
      "beta[27]    0.94    0.04    0.1   0.86   0.87    1.0    1.0    1.0    6.0    1.2\n",
      "beta[28]    0.96    0.05   0.11   0.86   0.89   1.01   1.01   1.02    6.0   1.17\n",
      "beta[29]    0.95    0.05   0.12   0.86   0.88   1.01   1.01   1.02    6.0   1.18\n",
      "beta[30]    0.93    0.03   0.11   0.86   0.88   0.98   0.98   0.99   10.0   1.11\n",
      "beta[31]    0.95    0.05   0.11   0.85   0.88   1.01   1.01   1.02    6.0   1.19\n",
      "beta[32]    0.95    0.04   0.11   0.86   0.89    1.0    1.0   1.01    7.0   1.16\n",
      "beta[33]    0.94    0.04    0.1   0.85   0.87   0.99   0.99    1.0    6.0   1.18\n",
      "beta[34]    0.97    0.04   0.11   0.87    0.9   1.02   1.03   1.03    6.0   1.19\n",
      "beta[35]    0.93    0.03    0.1   0.85   0.88   0.98   0.98   0.99    8.0   1.14\n",
      "beta[36]    0.97    0.04    0.1   0.87   0.91   1.01   1.02   1.02    7.0   1.17\n",
      "beta[37]    0.94    0.04    0.1   0.85   0.88   0.98   0.99   0.99    6.0   1.19\n",
      "beta[38]    0.96    0.04    0.1   0.87   0.91   1.01   1.01   1.02    7.0   1.16\n",
      "beta[39]    0.95    0.04   0.11   0.86   0.88    1.0    1.0   1.01    6.0   1.17\n",
      "beta[40]    0.95    0.04   0.11   0.86   0.89   0.99    1.0    1.0    8.0   1.15\n",
      "beta[41]    0.95    0.04   0.11   0.86   0.88    1.0    1.0   1.01    7.0   1.15\n",
      "beta[42]    0.95    0.04   0.11   0.86   0.88    1.0    1.0    1.0    7.0   1.16\n",
      "beta[43]    0.96    0.05   0.12   0.86   0.88   1.02   1.02   1.03    5.0    1.2\n",
      "beta[44]    0.93    0.04   0.11   0.85   0.87   0.98   0.98   0.99    8.0   1.14\n",
      "beta[45]    0.94    0.04   0.11   0.85   0.89    1.0    1.0    1.0    9.0   1.13\n",
      "beta[46]    0.96    0.05   0.13   0.86   0.88   1.02   1.03   1.03    6.0   1.18\n",
      "beta[47]    0.94    0.04   0.12   0.86   0.88   0.99   0.99    1.0   11.0   1.11\n",
      "beta[48]    0.95    0.04   0.13   0.86   0.88   1.01   1.01   1.02   10.0   1.11\n",
      "beta[49]    0.94    0.04   0.13   0.85   0.88    1.0    1.0    1.0   11.0    1.1\n",
      "beta[50]    0.94    0.04   0.13   0.86   0.88   0.99   0.99    1.0   14.0   1.09\n",
      "beta[51]    0.94    0.04   0.13   0.86   0.88    1.0    1.0   1.01   12.0    1.1\n",
      "beta[52]    0.95    0.04   0.14   0.85   0.89    1.0   1.01   1.01   11.0   1.11\n",
      "beta[53]    0.95    0.04   0.14   0.86   0.89   1.01   1.02   1.02   11.0   1.11\n",
      "beta[54]    0.93    0.04   0.13   0.86   0.87   0.98   0.98   0.99   14.0   1.09\n",
      "beta[55]    0.95    0.04   0.14   0.87   0.89   1.01   1.02   1.02   10.0   1.11\n",
      "beta[56]    0.94    0.04   0.13   0.85   0.88   0.99   0.99    1.0   12.0    1.1\n",
      "beta[57]    0.94    0.04   0.13   0.86   0.88    1.0    1.0   1.01   10.0   1.11\n",
      "beta[58]    0.95    0.04   0.13   0.85   0.89   1.01   1.01   1.02   10.0   1.12\n",
      "beta[59]    0.93    0.04   0.14   0.85   0.87   0.99   0.99    1.0   13.0   1.09\n",
      "beta[60]    0.96    0.04   0.14   0.86    0.9   1.01   1.02   1.02   11.0    1.1\n",
      "beta[61]    0.93    0.04   0.14   0.84   0.87   0.99   0.99    1.0   12.0    1.1\n",
      "beta[62]    0.95    0.04   0.14   0.87    0.9   1.01   1.01   1.02   15.0   1.08\n",
      "beta[63]    0.94    0.04   0.14   0.85   0.87   0.99    1.0    1.0   12.0    1.1\n",
      "beta[64]    0.95    0.04   0.15   0.86   0.89   1.01   1.01   1.01   14.0   1.09\n",
      "beta[65]    0.94    0.04   0.15   0.85   0.88   0.99    1.0    1.0   14.0   1.09\n",
      "beta[66]    0.94    0.04   0.14   0.85   0.88   0.99   0.99    1.0   13.0   1.09\n",
      "beta[67]    0.95    0.04   0.14   0.86   0.89   1.01   1.01   1.02   11.0   1.11\n",
      "beta[68]    0.94    0.04   0.13   0.85   0.88    1.0    1.0    1.0   12.0    1.1\n",
      "beta[69]    0.94    0.04   0.13   0.86   0.88    1.0    1.0    1.0    9.0   1.12\n",
      "beta[70]    0.95    0.04   0.13   0.85   0.89    1.0   1.01   1.01   10.0   1.12\n",
      "beta[71]    0.94    0.04   0.12   0.86   0.88    1.0    1.0   1.01    9.0   1.13\n",
      "beta[72]    0.95    0.04   0.12   0.85   0.88    1.0    1.0   1.01    8.0   1.14\n",
      "beta[73]    0.95    0.04   0.12   0.86   0.88    1.0   1.01   1.01    7.0   1.16\n",
      "beta[74]    0.94    0.04   0.11   0.85   0.88   0.99   0.99    1.0    7.0   1.15\n",
      "beta[75]    0.95    0.05   0.11   0.85   0.87   1.01   1.01   1.02    5.0    1.2\n",
      "beta[76]    0.94    0.04   0.11   0.85   0.87   0.99    1.0    1.0    6.0   1.18\n",
      "beta[77]    0.95    0.05   0.11   0.86   0.88   1.01   1.01   1.02    6.0    1.2\n",
      "beta[78]    0.93    0.04    0.1   0.84   0.87   0.98   0.98   0.99    6.0   1.17\n",
      "beta[79]    0.96    0.05   0.11   0.86   0.89   1.02   1.03   1.03    5.0   1.24\n",
      "beta[80]    0.93    0.04   0.11   0.84   0.86   0.98   0.99   0.99    6.0   1.18\n",
      "beta[81]    0.95    0.04   0.11   0.86   0.88    1.0   1.01   1.01    6.0   1.19\n",
      "beta[82]    0.94    0.05   0.11   0.84   0.87    1.0    1.0   1.01    5.0   1.21\n",
      "beta[83]    0.96    0.05   0.11   0.86   0.88   1.01   1.01   1.02    5.0   1.21\n",
      "beta[84]    0.94    0.05   0.11   0.85   0.87   0.99    1.0    1.0    5.0    1.2\n",
      "beta[85]    0.94    0.04   0.11   0.85   0.88   0.99   0.99    1.0    7.0   1.17\n",
      "beta[86]    0.95    0.05   0.12   0.85   0.86   1.01   1.01   1.02    5.0   1.23\n",
      "beta[87]    0.95    0.05   0.12   0.84   0.87   1.01   1.01   1.02    6.0   1.19\n",
      "beta[88]    0.93    0.04   0.12   0.85   0.86   0.98   0.98   0.99    8.0   1.14\n",
      "beta[89]    0.94    0.04   0.12   0.86   0.87   0.99    1.0    1.0    9.0   1.13\n",
      "beta[90]    0.95    0.05   0.13   0.84   0.87   1.01   1.02   1.02    7.0   1.16\n",
      "beta[91]    0.94    0.04   0.14   0.85   0.88    1.0    1.0   1.01   12.0    1.1\n",
      "beta[92]    0.93    0.04   0.14   0.84   0.87   0.98   0.98   0.99   14.0   1.09\n",
      "beta[93]    0.98    0.05   0.15   0.87   0.89   1.05   1.05   1.06    9.0   1.13\n",
      "beta[94]     0.9    0.03   0.14   0.84   0.86   0.95   0.95   0.96   26.0   1.06\n",
      "beta[95]    0.96    0.04   0.15   0.86    0.9   1.02   1.03   1.03   14.0   1.09\n",
      "beta[96]    0.93    0.04   0.15   0.85   0.87   0.99   0.99    1.0   15.0   1.09\n",
      "beta[97]    0.96    0.04   0.16   0.87   0.89   1.02   1.03   1.03   14.0   1.09\n",
      "beta[98]    0.92    0.03   0.16   0.84   0.87   0.97   0.97   0.98   24.0   1.06\n",
      "beta[99]    0.95    0.04   0.17   0.87   0.89   1.02   1.02   1.03   18.0   1.07\n",
      "beta[100]   0.93    0.03   0.17   0.85   0.89   0.98   0.99   0.99   27.0   1.06\n",
      "beta[101]   0.95    0.04   0.17   0.86   0.89   1.01   1.01   1.02   22.0   1.06\n",
      "beta[102]   0.94    0.03   0.17   0.86   0.88   0.99    1.0    1.0   23.0   1.06\n",
      "beta[103]   0.94    0.03   0.17   0.86   0.89   0.99   0.99    1.0   26.0   1.06\n",
      "beta[104]   0.96    0.04   0.17   0.87   0.89   1.02   1.03   1.03   17.0   1.08\n",
      "beta[105]   0.93    0.03   0.17   0.84   0.88   0.99   0.99    1.0   25.0   1.06\n",
      "beta[106]   0.95    0.03   0.17   0.87   0.89    1.0   1.01   1.01   25.0   1.06\n",
      "beta[107]   0.93    0.03   0.18   0.86   0.88   0.99    1.0    1.0   26.0   1.06\n",
      "beta[108]   0.95    0.04   0.19   0.87   0.89   1.02   1.02   1.03   23.0   1.06\n",
      "beta[109]   0.93    0.03    0.2   0.86   0.88   0.99   0.99    1.0   41.0   1.04\n",
      "beta[110]   0.93    0.03   0.21   0.86   0.89   0.99   0.99    1.0   48.0   1.04\n",
      "beta[111]   0.94    0.04   0.23   0.86   0.89   1.01   1.01   1.02   40.0   1.04\n",
      "beta[112]   0.94    0.03   0.24   0.87    0.9    1.0   1.01   1.01   50.0   1.04\n",
      "beta[113]   0.92    0.03   0.24   0.86   0.89   0.99   0.99    1.0   58.0   1.03\n",
      "beta[114]   0.94    0.03   0.24   0.87    0.9   1.01   1.01   1.02   50.0   1.04\n",
      "beta[115]   0.93    0.03   0.24   0.86   0.89    1.0    1.0   1.01   52.0   1.04\n",
      "beta[116]   0.94    0.03   0.25   0.86    0.9   1.01   1.01   1.02   53.0   1.04\n",
      "beta[117]   0.93    0.03   0.24   0.86   0.89   0.99    1.0    1.0   54.0   1.04\n",
      "beta[118]   0.94    0.04   0.24   0.86   0.89   1.01   1.01   1.02   47.0   1.04\n",
      "beta[119]   0.92    0.03   0.24   0.86   0.89   0.99   0.99    1.0   58.0   1.03\n",
      "beta[120]   0.95    0.04   0.25   0.87    0.9   1.02   1.02   1.03   44.0   1.04\n",
      "beta[121]   0.92    0.03   0.25   0.85   0.89   0.98   0.99   0.99   60.0   1.03\n",
      "beta[122]   0.95    0.04   0.27   0.87    0.9   1.02   1.02   1.03   52.0   1.04\n",
      "beta[123]   0.92    0.03   0.27   0.85   0.89   0.98   0.99   0.99   66.0   1.03\n",
      "beta[124]   0.94    0.04   0.28   0.87    0.9   1.01   1.02   1.02   58.0   1.03\n",
      "beta[125]   0.92    0.03   0.28   0.85   0.89   0.99    1.0    1.0   64.0   1.03\n",
      "beta[126]   0.93    0.03   0.28   0.87    0.9   0.99    1.0    1.0   67.0   1.03\n",
      "beta[127]   0.92    0.03   0.27   0.86    0.9   0.99   0.99    1.0   66.0   1.03\n",
      "beta[128]   0.94    0.04   0.28   0.87    0.9   1.01   1.02   1.02   57.0   1.03\n",
      "beta[129]   0.94    0.04   0.28   0.87    0.9   1.01   1.02   1.02   60.0   1.03\n",
      "beta[130]   0.92    0.03   0.26   0.87   0.89   0.98   0.98   0.99   68.0   1.03\n",
      "beta[131]   0.94    0.03   0.26   0.87    0.9   1.01   1.01   1.02   57.0   1.03\n",
      "beta[132]   0.93    0.03   0.25   0.86   0.89   0.99    1.0    1.0   56.0   1.03\n",
      "beta[133]   0.94    0.03   0.24   0.87    0.9    1.0   1.01   1.01   54.0   1.04\n",
      "beta[134]   0.93    0.03   0.24   0.85   0.89   0.99   0.99    1.0   56.0   1.03\n",
      "beta[135]   0.94    0.03   0.23   0.87    0.9   1.01   1.01   1.02   47.0   1.04\n",
      "beta[136]   0.93    0.03   0.23   0.85   0.89   0.99    1.0    1.0   48.0   1.04\n",
      "beta[137]   0.94    0.03   0.23   0.86   0.89    1.0   1.01   1.01   44.0   1.04\n",
      "beta[138]   0.93    0.03   0.22   0.84   0.88   0.99   0.99    1.0   42.0   1.04\n",
      "beta[139]   0.94    0.04   0.22   0.87   0.89   1.01   1.01   1.02   41.0   1.04\n",
      "beta[140]   0.93    0.03   0.21   0.86   0.88   0.99   0.99    1.0   41.0   1.04\n",
      "beta[141]   0.95    0.03   0.21   0.86    0.9   1.01   1.01   1.02   40.0   1.04\n",
      "beta[142]   0.93    0.03   0.21   0.85   0.88   0.98   0.99   0.99   44.0   1.04\n",
      "beta[143]   0.96    0.04   0.21   0.88   0.91   1.02   1.03   1.03   35.0   1.05\n",
      "beta[144]   0.92    0.03    0.2   0.86   0.88   0.97   0.98   0.99   46.0   1.04\n",
      "beta[145]   0.96    0.03   0.19   0.88   0.91   1.02   1.03   1.03   32.0   1.05\n",
      "beta[146]   0.92    0.03   0.19   0.86   0.88   0.98   0.98   0.99   38.0   1.05\n",
      "beta[147]   0.94    0.03   0.19   0.88    0.9    1.0    1.0   1.01   39.0   1.04\n",
      "beta[148]   0.94    0.03    0.2   0.86   0.89    1.0   1.01   1.01   32.0   1.05\n",
      "beta[149]   0.95    0.04    0.2   0.87    0.9   1.01   1.02   1.02   33.0   1.05\n",
      "beta[150]   0.92    0.03    0.2   0.86   0.88   0.98   0.99   0.99   42.0   1.04\n",
      "beta[151]   0.95    0.04   0.22   0.87   0.89   1.03   1.03   1.03   30.0   1.05\n",
      "beta[152]   0.92    0.03   0.24   0.85   0.88   0.99   0.99    1.0   54.0   1.04\n",
      "beta[153]   0.92    0.03   0.26   0.86   0.89   0.99   0.99    1.0   62.0   1.03\n",
      "beta[154]   0.93    0.04   0.28   0.86   0.89    1.0    1.0   1.01   61.0   1.03\n",
      "beta[155]   0.93    0.03   0.26   0.87   0.89   0.99    1.0    1.0   63.0   1.03\n",
      "beta[156]   0.93    0.03   0.26   0.86    0.9    1.0   1.01   1.01   55.0   1.04\n",
      "beta[157]   0.94    0.04   0.26   0.86    0.9   1.01   1.01   1.02   56.0   1.04\n",
      "beta[158]   0.93    0.03   0.26   0.87    0.9    1.0    1.0   1.01   60.0   1.03\n",
      "beta[159]   0.92    0.03   0.25   0.86   0.89   0.99   0.99    1.0   60.0   1.03\n",
      "beta[160]   0.94    0.03   0.24   0.87    0.9   1.01   1.01   1.02   50.0   1.04\n",
      "beta[161]   0.93    0.03   0.24   0.85   0.89   0.99    1.0    1.0   53.0   1.04\n",
      "beta[162]   0.94    0.03   0.23   0.87   0.89    1.0    1.0   1.01   48.0   1.04\n",
      "beta[163]   0.94    0.03   0.23   0.86   0.89    1.0    1.0   1.01   45.0   1.04\n",
      "beta[164]   0.94    0.03   0.23   0.87   0.89   1.01   1.01   1.02   44.0   1.04\n",
      "beta[165]   0.93    0.03   0.22   0.86   0.89   0.99    1.0    1.0   50.0   1.04\n",
      "beta[166]   0.94    0.03   0.22   0.87   0.89    1.0    1.0   1.01   42.0   1.04\n",
      "beta[167]   0.93    0.03   0.21   0.86   0.89   0.99   0.99    1.0   47.0   1.04\n",
      "beta[168]   0.94    0.04   0.21   0.86   0.88    1.0   1.01   1.01   34.0   1.05\n",
      "beta[169]   0.95    0.04   0.21   0.86   0.89   1.01   1.02   1.02   32.0   1.05\n",
      "beta[170]   0.92    0.03    0.2   0.85   0.88   0.98   0.98   0.99   43.0   1.04\n",
      "beta[171]   0.94    0.04    0.2   0.86   0.89   1.01   1.01   1.02   31.0   1.05\n",
      "beta[172]   0.93    0.03   0.18   0.86   0.88   0.99   0.99    1.0   31.0   1.05\n",
      "beta[173]   0.95    0.04   0.19   0.86   0.89   1.01   1.02   1.02   23.0   1.06\n",
      "beta[174]   0.94    0.04   0.18   0.86   0.88    1.0    1.0   1.01   25.0   1.06\n",
      "beta[175]   0.94    0.04   0.18   0.86   0.89    1.0    1.0   1.01   25.0   1.06\n",
      "beta[176]   0.93    0.03   0.18   0.85   0.88   0.99   0.99    1.0   27.0   1.06\n",
      "beta[177]    5.9    5.78  11.55 -16.87  -4.06   6.13  18.25  18.94    4.0   1.48\n",
      "u1          0.99  1.5e-3 6.5e-3   0.97   0.98   0.98   0.99    1.0   18.0   1.12\n",
      "u0          0.06  5.5e-3   0.02 7.1e-3   0.05   0.06   0.07    0.1   17.0   1.13\n",
      "b[0]        1.27    0.62   0.87   0.07   0.21   1.79   1.94   2.37    2.0   2.67\n",
      "b[1]       -0.39    0.27   0.46  -0.95  -0.74  -0.62   0.07   0.26    3.0   1.74\n",
      "b[2]        0.12    0.32   0.45  -0.43  -0.23  -0.15   0.68   0.73    2.0   3.13\n",
      "sb          0.01  1.9e-3 6.0e-3   0.01   0.01   0.01   0.02   0.02   10.0   1.12\n",
      "su          0.13  2.9e-3 6.4e-3   0.12   0.13   0.13   0.14   0.14    5.0   1.21\n",
      "s         8.3e-3  5.0e-3 7.1e-3 1.3e-3 2.7e-3 4.0e-3   0.02   0.02    2.0   2.35\n",
      "lp__      2162.6  104.57 147.88 1968.1 2004.5 2205.4 2272.2 2404.7    2.0    1.8\n",
      "\n",
      "Samples were drawn using NUTS(diag_e) at Sun Sep 25 01:38:01 2016.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "smodel_ub2 = pystan.StanModel(model_code=model_ub2)\n",
    "fit_ub2 = smodel_ub2.sampling(data, iter=31000, warmup=1000, thin=10, chains=3, seed=71)\n",
    "res_ub2=fit_ub2.extract()\n",
    "print fit_ub2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ub2l=\"\"\"\n",
    "data{\n",
    "        int<lower=1> N;\n",
    "        real<lower=0,upper=100> pi[N];\n",
    "        real<lower=0,upper=100> u[N];\n",
    "}\n",
    "\n",
    "parameters{\n",
    "    real L; //lambda*phi    \n",
    "    real<lower=0> un; //natural unemployment rate\n",
    "    real<lower=-20,upper=20> beta[N];    \n",
    "    real u1;\n",
    "    real u0;\n",
    "    real b[3];\n",
    "    real<lower=0> sb;\n",
    "    real<lower=0> su;\n",
    "    real<lower=0> s;\n",
    "}\n",
    "\n",
    "model{\n",
    "    real p[N-1];\n",
    "    \n",
    "    for(i in 2:(N-1))\n",
    "        u[i]~lognormal(u0+u1*u[i-1],su);\n",
    "\n",
    "    for(i in 3:(N-1))\n",
    "        beta[i]~normal(b[1]+b[2]*beta[i-1]+b[3]*beta[i-2],sb);\n",
    "\n",
    "    for(i in 1:(N-1))\n",
    "        p[i]<-beta[i]*pi[i+1]-L*(u[i]-un);\n",
    "        \n",
    "    for(i in 1:(N-1))\n",
    "        pi[i]~normal(p[i],s);\n",
    "}         \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_83767b8d5753f2ea5ad4515ef703e6fc.\n",
      "3 chains, each with iter=31000; warmup=1000; thin=10; \n",
      "post-warmup draws per chain=3000, total post-warmup draws=9000.\n",
      "\n",
      "            mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "L         7.4e-4  4.4e-4 7.6e-4-3.6e-4 2.8e-4 5.3e-4 1.1e-3 2.2e-3    3.0    nan\n",
      "un         17.27   11.56  16.35    0.1   0.55  13.71   41.8  42.59    2.0   4.01\n",
      "beta[0]     1.03  6.9e-3 9.8e-3   1.01   1.03   1.03   1.04   1.05    2.0   1.56\n",
      "beta[1]     0.96  6.8e-3 9.7e-3   0.94   0.95   0.95   0.97   0.97    2.0   1.76\n",
      "beta[2]     1.01  6.3e-3   0.01   0.98    1.0   1.01   1.02   1.02    3.0   1.55\n",
      "beta[3]     0.97  7.7e-3   0.01   0.95   0.96   0.97   0.98   0.99    2.0   1.96\n",
      "beta[4]     0.99  7.7e-3   0.01   0.97   0.99   0.99    1.0   1.01    2.0    1.8\n",
      "beta[5]      1.0    0.01   0.02   0.96   0.98    1.0   1.02   1.02    2.0    3.5\n",
      "beta[6]     0.98  4.5e-3 9.0e-3   0.96   0.97   0.98   0.98    1.0    4.0   1.47\n",
      "beta[7]     0.99  5.4e-3 9.4e-3   0.97   0.99   0.99    1.0    1.0    3.0   1.45\n",
      "beta[8]     0.99  6.5e-3   0.01   0.96   0.98   0.99    1.0    1.0    3.0   2.08\n",
      "beta[9]      1.0  5.0e-3 8.6e-3   0.98   0.99    1.0    1.0   1.01    3.0   1.64\n",
      "beta[10]    0.96  4.9e-3 8.4e-3   0.94   0.95   0.96   0.97   0.97    3.0   1.48\n",
      "beta[11]    1.07    0.02   0.03   1.03   1.04   1.09    1.1    1.1    2.0   4.46\n",
      "beta[12]    0.88    0.02   0.03   0.84   0.86   0.86   0.91   0.93    2.0   5.12\n",
      "beta[13]    1.05    0.01   0.02   1.01   1.03   1.05   1.06   1.07    2.0   2.85\n",
      "beta[14]    1.02    0.02   0.02   0.98    1.0   1.03   1.05   1.05    2.0   4.34\n",
      "beta[15]    0.99  6.3e-3 8.9e-3   0.98   0.99   0.99    1.0   1.01    2.0   1.72\n",
      "beta[16]    0.99  4.9e-3 9.7e-3   0.97   0.99    1.0    1.0   1.01    4.0   1.55\n",
      "beta[17]    0.94  9.0e-3   0.01   0.92   0.94   0.94   0.95   0.97    2.0   2.29\n",
      "beta[18]    1.05    0.02   0.02   1.01   1.03   1.06   1.08   1.08    2.0    4.0\n",
      "beta[19]    0.95  5.2e-3 9.0e-3   0.94   0.95   0.95   0.96   0.97    3.0    1.5\n",
      "beta[20]    1.01  5.0e-3   0.01   0.99    1.0   1.01   1.02   1.02    4.0    1.4\n",
      "beta[21]     1.0  8.4e-3   0.01   0.97   0.99    1.0   1.01   1.01    2.0    2.0\n",
      "beta[22]    0.98  7.4e-3   0.01   0.95   0.97   0.98   0.98    1.0    2.0   1.74\n",
      "beta[23]    1.01  8.4e-3   0.01   0.99   1.01   1.01   1.03   1.03    2.0   2.02\n",
      "beta[24]    0.98  5.6e-3 9.7e-3   0.96   0.98   0.99   0.99    1.0    3.0   1.64\n",
      "beta[25]     1.0  5.7e-3 9.8e-3   0.98   0.99    1.0   1.01   1.01    3.0   1.45\n",
      "beta[26]    0.98  5.8e-310.0e-3   0.97   0.98   0.98   0.99    1.0    3.0   1.65\n",
      "beta[27]    0.99  5.6e-3 9.8e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.58\n",
      "beta[28]     1.0  9.3e-3   0.01   0.97   0.99    1.0   1.01   1.02    2.0   2.08\n",
      "beta[29]     1.0  8.5e-3   0.01   0.97   0.99    1.0   1.01   1.01    2.0   1.83\n",
      "beta[30]    0.98  5.9e-3   0.01   0.96   0.97   0.98   0.98    1.0    3.0   1.49\n",
      "beta[31]     1.0  8.5e-3   0.01   0.97   0.99    1.0   1.01   1.01    2.0   2.25\n",
      "beta[32]    0.99  5.2e-3 9.0e-3   0.98   0.99   0.99    1.0   1.01    3.0   1.43\n",
      "beta[33]    0.98  4.9e-3 8.5e-3   0.96   0.98   0.98   0.99   0.99    3.0   1.43\n",
      "beta[34]    1.01  8.6e-3   0.01   0.99    1.0   1.01   1.02   1.03    2.0   1.94\n",
      "beta[35]    0.97  4.3e-3 8.5e-3   0.95   0.97   0.97   0.98   0.98    4.0   1.53\n",
      "beta[36]    1.01  7.3e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.58\n",
      "beta[37]    0.98  5.0e-3 8.7e-3   0.96   0.97   0.98   0.99   0.99    3.0   1.62\n",
      "beta[38]     1.0  7.2e-3   0.01   0.98   0.99    1.0   1.01   1.02    2.0   1.81\n",
      "beta[39]    0.99  8.1e-3   0.01   0.97   0.98   0.99    1.0   1.01    2.0   2.04\n",
      "beta[40]    0.99  6.3e-3 8.9e-3   0.97   0.98   0.99    1.0    1.0    2.0   1.65\n",
      "beta[41]    0.99  5.2e-3 9.0e-3   0.97   0.99   0.99    1.0   1.01    3.0    1.5\n",
      "beta[42]    0.99  7.0e-3 9.9e-3   0.96   0.98   0.99    1.0    1.0    2.0   1.76\n",
      "beta[43]    1.01  8.4e-3   0.01   0.98    1.0   1.01   1.02   1.02    2.0   1.98\n",
      "beta[44]    0.97  4.5e-3 9.0e-3   0.96   0.97   0.98   0.98   0.99    4.0   1.46\n",
      "beta[45]    0.99  4.9e-3 8.5e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.37\n",
      "beta[46]    1.01  8.6e-3   0.01   0.99    1.0   1.01   1.02   1.03    2.0   2.32\n",
      "beta[47]    0.98  4.0e-3 8.9e-3   0.96   0.98   0.98   0.99    1.0    5.0   1.43\n",
      "beta[48]     1.0  5.4e-3 9.3e-3   0.98   0.99    1.0   1.01   1.01    3.0    1.5\n",
      "beta[49]    0.99  5.9e-3   0.01   0.97   0.98   0.99    1.0    1.0    3.0   1.39\n",
      "beta[50]    0.98  5.4e-3 9.3e-3   0.96   0.98   0.99   0.99    1.0    3.0    1.3\n",
      "beta[51]    0.99  5.5e-3 9.5e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.34\n",
      "beta[52]    0.99  9.0e-3   0.01   0.96   0.98   0.99    1.0   1.01    2.0   1.92\n",
      "beta[53]     1.0  7.8e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.64\n",
      "beta[54]    0.97  5.0e-3 8.7e-3   0.96   0.97   0.97   0.98   0.99    3.0   1.45\n",
      "beta[55]     1.0  8.7e-3   0.01   0.98   0.99    1.0   1.01   1.02    2.0    2.0\n",
      "beta[56]    0.98  6.3e-3   0.01   0.96   0.98   0.98   0.99    1.0    3.0   1.49\n",
      "beta[57]    0.99  6.9e-3 9.7e-3   0.97   0.98   0.99    1.0    1.0    2.0   1.64\n",
      "beta[58]     1.0  8.0e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0    1.7\n",
      "beta[59]    0.98  5.1e-3 8.8e-3   0.96   0.97   0.98   0.99   0.99    3.0   1.41\n",
      "beta[60]     1.0  6.1e-3   0.01   0.98    1.0    1.0   1.01   1.02    3.0   1.49\n",
      "beta[61]    0.98  5.4e-3 9.4e-3   0.96   0.97   0.98   0.99   0.99    3.0   1.48\n",
      "beta[62]     1.0  5.2e-3 8.9e-3   0.98    1.0    1.0   1.01   1.01    3.0   1.57\n",
      "beta[63]    0.99  6.0e-3   0.01   0.96   0.98   0.99   0.99    1.0    3.0   1.52\n",
      "beta[64]     1.0  7.5e-3   0.01   0.98   0.99    1.0   1.01   1.01    2.0   1.78\n",
      "beta[65]    0.99  5.3e-3 9.2e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.38\n",
      "beta[66]    0.98  5.8e-3   0.01   0.96   0.98   0.98   0.99    1.0    3.0   1.38\n",
      "beta[67]     1.0  7.2e-3   0.01   0.98   0.99    1.0   1.01   1.01    2.0   1.68\n",
      "beta[68]    0.99  5.5e-3 9.5e-3   0.97   0.98   0.99    1.0    1.0    3.0    1.4\n",
      "beta[69]    0.99  5.6e-3 9.7e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.53\n",
      "beta[70]    0.99  6.9e-3 9.7e-3   0.97   0.99   0.99    1.0   1.01    2.0   1.69\n",
      "beta[71]    0.99  7.6e-3   0.01   0.97   0.98   0.99    1.0    1.0    2.0   1.63\n",
      "beta[72]    0.99  7.4e-3   0.01   0.97   0.98   0.99    1.0    1.0    2.0   1.67\n",
      "beta[73]    0.99  7.1e-310.0e-3   0.97   0.99   0.99    1.0   1.01    2.0   1.77\n",
      "beta[74]    0.98  5.5e-3 9.6e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.54\n",
      "beta[75]     1.0  4.8e-3 9.6e-3   0.98   0.99    1.0   1.01   1.01    4.0   1.67\n",
      "beta[76]    0.98  8.4e-3   0.01   0.96   0.98   0.98    1.0    1.0    2.0   1.81\n",
      "beta[77]     1.0  5.5e-3 9.5e-3   0.98    1.0    1.0   1.01   1.02    3.0   1.45\n",
      "beta[78]    0.98  5.3e-3 9.1e-3   0.96   0.97   0.98   0.98   0.99    3.0   1.36\n",
      "beta[79]    1.01  8.2e-3   0.01   0.99   1.01   1.01   1.02   1.03    2.0   1.69\n",
      "beta[80]    0.98  5.5e-3 9.4e-3   0.96   0.97   0.98   0.98   0.99    3.0   1.43\n",
      "beta[81]     1.0  5.2e-3 9.0e-3   0.98   0.99    1.0    1.0   1.01    3.0   1.53\n",
      "beta[82]    0.99  5.9e-3   0.01   0.97   0.99   0.99    1.0   1.01    3.0   1.52\n",
      "beta[83]     1.0  7.3e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.78\n",
      "beta[84]    0.99  5.1e-3 8.8e-3   0.97   0.98   0.99   0.99    1.0    3.0   1.37\n",
      "beta[85]    0.99  5.4e-3 9.4e-3   0.97   0.98   0.99   0.99   1.01    3.0   1.31\n",
      "beta[86]     1.0  8.8e-3   0.01   0.97   0.99    1.0   1.01   1.02    2.0   1.86\n",
      "beta[87]     1.0  8.5e-3   0.01   0.97   0.99    1.0   1.01   1.01    2.0   1.86\n",
      "beta[88]    0.98  5.3e-3 9.2e-3   0.96   0.97   0.98   0.98    1.0    3.0    1.6\n",
      "beta[89]    0.99  5.0e-3 8.7e-3   0.97   0.98   0.99   0.99    1.0    3.0    1.5\n",
      "beta[90]     1.0  9.2e-3   0.01   0.97   0.99    1.0   1.01   1.02    2.0   1.99\n",
      "beta[91]    0.99  5.3e-3 9.1e-3   0.97   0.98   0.99    1.0   1.01    3.0   1.59\n",
      "beta[92]    0.97  5.2e-3 9.0e-3   0.96   0.97   0.97   0.98   0.99    3.0   1.52\n",
      "beta[93]    1.03    0.01   0.02    1.0   1.02   1.04   1.05   1.05    2.0   2.21\n",
      "beta[94]    0.95  5.6e-3 9.8e-3   0.93   0.94   0.95   0.95   0.97    3.0   1.62\n",
      "beta[95]    1.01  6.3e-3   0.01   0.99   1.01   1.01   1.02   1.03    3.0   1.73\n",
      "beta[96]    0.98  7.7e-3   0.01   0.96   0.97   0.98   0.99    1.0    2.0   2.09\n",
      "beta[97]    1.01  8.8e-3   0.01   0.98   1.01   1.01   1.02   1.03    2.0   1.65\n",
      "beta[98]    0.97  4.2e-3 8.5e-3   0.95   0.96   0.97   0.97   0.98    4.0   1.36\n",
      "beta[99]    1.01  7.2e-3   0.01   0.97    1.0   1.01   1.02   1.02    3.0   1.63\n",
      "beta[100]   0.98  4.9e-3 8.5e-3   0.96   0.97   0.98   0.99   0.99    3.0   1.53\n",
      "beta[101]    1.0  6.4e-3   0.01   0.98    1.0    1.0   1.01   1.02    3.0   1.47\n",
      "beta[102]   0.99  5.2e-3 8.9e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.45\n",
      "beta[103]   0.98  5.1e-3 8.8e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.36\n",
      "beta[104]   1.01  8.9e-3   0.01   0.98    1.0   1.01   1.02   1.03    2.0   1.89\n",
      "beta[105]   0.98  5.0e-3 8.6e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.42\n",
      "beta[106]    1.0  5.2e-3 9.0e-3   0.98   0.99    1.0    1.0   1.01    3.0   1.42\n",
      "beta[107]   0.99  5.5e-3 9.5e-3   0.97   0.98   0.99   0.99    1.0    3.0   1.52\n",
      "beta[108]   1.01  7.9e-3   0.01   0.98    1.0   1.01   1.02   1.02    2.0   1.66\n",
      "beta[109]   0.98  5.3e-3 9.2e-3   0.97   0.98   0.98   0.99    1.0    3.0   1.57\n",
      "beta[110]   0.99  4.8e-3 8.3e-3   0.97   0.98   0.99   0.99    1.0    3.0   1.43\n",
      "beta[111]    1.0  8.1e-3   0.01   0.98   0.99    1.0   1.01   1.02    2.0   2.05\n",
      "beta[112]   0.99  5.7e-3 9.9e-3   0.97   0.99   0.99    1.0   1.01    3.0   1.54\n",
      "beta[113]   0.98  5.2e-3 9.1e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.42\n",
      "beta[114]    1.0  7.7e-3   0.01   0.97   0.99    1.0   1.01   1.01    2.0   1.78\n",
      "beta[115]   0.99  5.2e-3 9.1e-3   0.97   0.98   0.99    1.0    1.0    3.0   1.43\n",
      "beta[116]    1.0  6.8e-3   0.01   0.97   0.99    1.0   1.01   1.01    3.0   1.66\n",
      "beta[117]   0.99  6.9e-3 9.8e-3   0.96   0.98   0.99   0.99    1.0    2.0   1.65\n",
      "beta[118]    1.0  5.4e-3 9.3e-3   0.98   0.99    1.0   1.01   1.01    3.0   1.38\n",
      "beta[119]   0.98  5.3e-3 9.2e-3   0.96   0.97   0.98   0.99   0.99    3.0   1.37\n",
      "beta[120]   1.01  6.2e-3   0.01   0.99    1.0   1.01   1.02   1.03    3.0   1.52\n",
      "beta[121]   0.98  5.2e-3 9.0e-3   0.96   0.97   0.98   0.99   0.99    3.0    1.4\n",
      "beta[122]   1.01  7.7e-3   0.01   0.99   1.01   1.01   1.02   1.03    2.0   1.51\n",
      "beta[123]   0.98  5.1e-3 8.9e-3   0.96   0.97   0.98   0.98   0.99    3.0    1.4\n",
      "beta[124]    1.0  7.4e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.61\n",
      "beta[125]   0.99  5.3e-3 9.2e-3   0.97   0.98   0.99   0.99    1.0    3.0    1.4\n",
      "beta[126]   0.99  5.2e-3 8.9e-3   0.97   0.98   0.99   0.99   1.01    3.0   1.34\n",
      "beta[127]   0.99  5.0e-3 8.6e-3   0.97   0.98   0.99   0.99    1.0    3.0   1.32\n",
      "beta[128]    1.0  8.1e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0    1.7\n",
      "beta[129]    1.0  7.5e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.69\n",
      "beta[130]   0.98  5.5e-3 9.5e-3   0.96   0.97   0.98   0.98    1.0    3.0   1.33\n",
      "beta[131]    1.0  5.7e-3 9.8e-3   0.98   0.99    1.0   1.01   1.02    3.0   1.55\n",
      "beta[132]   0.99  6.4e-3   0.01   0.96   0.98   0.99   0.99   1.01    3.0   1.32\n",
      "beta[133]    1.0  5.0e-3   0.01   0.98   0.99    1.0   1.01   1.01    4.0   1.28\n",
      "beta[134]   0.98  5.4e-3 9.4e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.36\n",
      "beta[135]    1.0  6.0e-3   0.01   0.98    1.0    1.0   1.01   1.02    3.0   1.45\n",
      "beta[136]   0.99  5.7e-3 9.8e-3   0.96   0.98   0.99   0.99    1.0    3.0   1.32\n",
      "beta[137]    1.0  5.4e-3 9.3e-3   0.98   0.99    1.0   1.01   1.01    3.0   1.42\n",
      "beta[138]   0.98  5.6e-3 9.7e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.52\n",
      "beta[139]    1.0  5.6e-3 9.7e-3   0.98    1.0    1.0   1.01   1.02    3.0   1.41\n",
      "beta[140]   0.98  5.5e-3 9.5e-3   0.96   0.98   0.98   0.99    1.0    3.0    1.5\n",
      "beta[141]    1.0  5.4e-3 9.4e-3   0.98    1.0    1.0   1.01   1.02    3.0   1.42\n",
      "beta[142]   0.98  5.2e-3 8.9e-3   0.96   0.97   0.98   0.99   0.99    3.0    1.5\n",
      "beta[143]   1.01  8.5e-3   0.01   0.99   1.01   1.01   1.02   1.03    2.0   1.88\n",
      "beta[144]   0.97  5.3e-3   0.01   0.94   0.96   0.97   0.98   0.98    4.0   1.51\n",
      "beta[145]   1.01  6.1e-3   0.01   0.99   1.01   1.02   1.02   1.03    3.0   1.45\n",
      "beta[146]   0.97  4.9e-3 9.7e-3   0.96   0.97   0.97   0.98   0.99    4.0   1.45\n",
      "beta[147]    1.0  5.5e-3 9.5e-3   0.98   0.99    1.0    1.0   1.01    3.0   1.35\n",
      "beta[148]   0.99  9.3e-3   0.01   0.96   0.98   0.99    1.0   1.01    2.0   1.95\n",
      "beta[149]    1.0  7.6e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.67\n",
      "beta[150]   0.98  5.1e-3 8.9e-3   0.96   0.97   0.98   0.99   0.99    3.0   1.47\n",
      "beta[151]   1.01    0.01   0.01   0.98    1.0   1.01   1.03   1.03    2.0   2.08\n",
      "beta[152]   0.98  4.2e-3 8.5e-3   0.96   0.98   0.98   0.99    1.0    4.0   1.32\n",
      "beta[153]   0.98  5.0e-3 8.6e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.35\n",
      "beta[154]   0.99  5.4e-3 9.4e-3   0.97   0.99   0.99    1.0   1.01    3.0   1.49\n",
      "beta[155]   0.99  4.3e-3 8.6e-3   0.97   0.98   0.99   0.99    1.0    4.0   1.26\n",
      "beta[156]    1.0  7.0e-3 9.9e-3   0.98   0.99    1.0    1.0   1.01    2.0   1.67\n",
      "beta[157]    1.0  7.1e-3   0.01   0.98   0.99    1.0   1.01   1.01    2.0   1.57\n",
      "beta[158]   0.99  5.1e-3 8.8e-3   0.97   0.99   0.99    1.0   1.01    3.0   1.54\n",
      "beta[159]   0.98  5.4e-3 9.3e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.38\n",
      "beta[160]    1.0  6.9e-3 9.7e-3   0.98    1.0    1.0   1.01   1.02    2.0   1.58\n",
      "beta[161]   0.99  5.8e-310.0e-3   0.96   0.98   0.99   0.99    1.0    3.0   1.54\n",
      "beta[162]   0.99  5.3e-3 9.1e-3   0.97   0.99   0.99    1.0   1.01    3.0    1.4\n",
      "beta[163]   0.99  6.2e-3   0.01   0.97   0.99   0.99    1.0   1.01    3.0   1.41\n",
      "beta[164]    1.0  6.4e-3   0.01   0.97   0.99    1.0   1.01   1.02    3.0   1.49\n",
      "beta[165]   0.99  5.4e-3 9.3e-3   0.97   0.98   0.99   0.99    1.0    3.0    1.6\n",
      "beta[166]    1.0  5.3e-3 9.2e-3   0.97   0.99    1.0    1.0   1.01    3.0    1.4\n",
      "beta[167]   0.98  4.5e-3 9.1e-3   0.96   0.98   0.98   0.99    1.0    4.0    1.4\n",
      "beta[168]    1.0  7.5e-3   0.01   0.97   0.99    1.0   1.01   1.01    2.0   1.53\n",
      "beta[169]    1.0  8.3e-3   0.01   0.98   0.99    1.0   1.01   1.02    2.0   1.87\n",
      "beta[170]   0.98  5.0e-3 8.7e-3   0.96   0.97   0.98   0.98   0.99    3.0   1.46\n",
      "beta[171]    1.0  6.8e-3   0.01   0.97   0.99    1.0   1.01   1.01    3.0   1.68\n",
      "beta[172]   0.98  5.0e-3 8.7e-3   0.96   0.98   0.98   0.99    1.0    3.0   1.44\n",
      "beta[173]    1.0  7.9e-3   0.01   0.98    1.0    1.0   1.01   1.02    2.0   1.82\n",
      "beta[174]   0.99  8.2e-3   0.01   0.96   0.98   0.99    1.0   1.01    2.0   1.77\n",
      "beta[175]   0.99  5.8e-310.0e-3   0.97   0.99   0.99    1.0   1.01    3.0   1.47\n",
      "beta[176]   0.98  5.2e-3 9.0e-3   0.96   0.98   0.99   0.99    1.0    3.0   1.43\n",
      "beta[177]   6.53     6.0   8.49 -10.34  -1.39   9.79  13.41  18.51    2.0   1.69\n",
      "u1          0.32  2.1e-3 4.2e-3   0.32   0.32   0.32   0.32   0.33    4.0   1.24\n",
      "u0          0.06  9.6e-3   0.02   0.03   0.04   0.05   0.07   0.08    3.0   1.44\n",
      "b[0]        1.74    0.56   0.79   0.64   0.64   2.25   2.32   2.49    2.0   9.27\n",
      "b[1]       -0.65    0.28   0.39  -1.03  -0.96   -0.9  -0.11  -0.09    2.0   8.52\n",
      "b[2]        -0.1    0.28    0.4  -0.48  -0.39  -0.35   0.44   0.47    2.0   8.55\n",
      "sb          0.01  7.1e-4 1.2e-3   0.01   0.01   0.01   0.01   0.02    3.0    nan\n",
      "su          0.09  2.6e-3 4.4e-3   0.08   0.08   0.09   0.09    0.1    3.0   1.46\n",
      "s         6.8e-3  4.0e-3 5.7e-3 1.0e-3 2.5e-3 3.5e-3   0.01   0.01    2.0    nan\n",
      "lp__      2255.8   105.9 149.77 2048.1 2067.1 2301.9 2355.6 2515.1    2.0   3.34\n",
      "\n",
      "Samples were drawn using NUTS(diag_e) at Sun Sep 25 02:40:27 2016.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "smodel_ub2l = pystan.StanModel(model_code=model_ub2l)\n",
    "fit_ub2l = smodel_ub2l.sampling(data, iter=31000, warmup=1000, thin=10, chains=3, seed=71)\n",
    "res_ub2l=fit_ub2l.extract()\n",
    "print fit_ub2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ubv=\"\"\"\n",
    "data{\n",
    "        int<lower=1> N;\n",
    "        real<lower=0,upper=100> pi[N];\n",
    "        real<lower=0,upper=100> u[N];\n",
    "}\n",
    "\n",
    "parameters{\n",
    "    real L; //lambda*phi    \n",
    "    real<lower=0> un[N-1]; //natural unemployment rate\n",
    "    real<lower=-20,upper=20> beta[N];    \n",
    "    real uc[2];\n",
    "    real unc[2];\n",
    "    real b[3];\n",
    "    real<lower=0> sb;\n",
    "    real<lower=0> su;\n",
    "    real<lower=0> sun;\n",
    "    real<lower=0> s;\n",
    "}\n",
    "\n",
    "model{\n",
    "    real p[N-1];\n",
    "    \n",
    "    for(i in 2:(N-1)){\n",
    "        u[i]~normal(uc[1]+uc[2]*u[i-1],su);\n",
    "\n",
    "        un[i]~normal(unc[1]+unc[2]*un[i-1],sun);\n",
    "    }\n",
    "    \n",
    "    for(i in 3:(N-1))\n",
    "        beta[i]~normal(b[1]+b[2]*beta[i-1]+b[3]*beta[i-2],sb);\n",
    "\n",
    "    for(i in 1:(N-1))\n",
    "        p[i]<-beta[i]*pi[i+1]-L*(u[i]-un[i]);\n",
    "        \n",
    "    for(i in 1:(N-1))\n",
    "        pi[i]~normal(p[i],s);\n",
    "}         \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_f515382c0fda9ce7475ae8911f56480e.\n",
      "3 chains, each with iter=31000; warmup=1000; thin=10; \n",
      "post-warmup draws per chain=3000, total post-warmup draws=9000.\n",
      "\n",
      "            mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "L         9.1e-4  5.5e-4 7.7e-4-3.8e-4 2.1e-4 8.3e-4 1.8e-3 1.9e-3    2.0    nan\n",
      "un[0]      51.18    5.93  47.09   2.91  16.88  49.92  59.73 197.31   63.0   1.03\n",
      "un[1]      27.13    13.2  18.66   1.39  11.67  21.87  47.47  51.88    2.0    1.6\n",
      "un[2]      27.03   12.32  17.42   4.21  12.52  19.34  49.03  52.01    2.0   2.61\n",
      "un[3]      25.99   11.95  16.89   3.99  12.04  18.75  47.92  51.84    2.0    3.0\n",
      "un[4]      26.22   12.15  17.19   3.95  13.18  17.89  48.16  52.58    2.0   3.43\n",
      "un[5]      26.12   12.04  17.03   3.97  13.07  18.48  48.93   51.4    2.0   3.49\n",
      "un[6]      26.16   12.22  17.28    3.7   12.5  17.74  49.18  51.98    2.0   3.49\n",
      "un[7]      25.91   11.81   16.7    3.5  12.82  19.07  47.47  50.93    2.0   3.32\n",
      "un[8]      25.77   12.24   17.3   3.61  12.19  18.01  48.68  51.88    2.0   3.52\n",
      "un[9]      25.93   11.89  16.82   3.79  12.52  18.87  48.05  50.95    2.0   3.38\n",
      "un[10]     25.59   12.32  17.43   3.32  11.47  17.77  48.74  51.61    2.0   3.43\n",
      "un[11]     26.65   12.07  17.07   3.42  13.26   19.1  48.97  52.16    2.0    3.3\n",
      "un[12]     24.59   12.16  17.19   3.05  10.49  16.41  46.41  51.36    2.0    3.4\n",
      "un[13]     27.31   12.49  17.67   3.27  13.41  20.92  50.42  53.42    2.0   3.28\n",
      "un[14]      26.7   12.57  17.78   3.43  13.08   18.3  50.21  53.52    2.0   3.35\n",
      "un[15]     26.99   12.16  17.19   3.36  13.45  20.86  48.96  52.92    2.0   3.07\n",
      "un[16]     26.03   12.32  17.43   3.17   12.1  18.01  49.31  51.82    2.0   3.37\n",
      "un[17]     26.03   12.16   17.2   3.26  12.73  18.59  48.82  52.03    2.0   3.48\n",
      "un[18]     26.17   12.53  17.71   3.73  11.34  18.83  49.01  52.91    2.0   3.29\n",
      "un[19]     25.73   12.15  17.18   3.14  12.36  18.02  48.45  51.47    2.0   3.47\n",
      "un[20]     26.14   12.29  17.37   3.68  12.54  17.49  48.91  52.33    2.0   3.41\n",
      "un[21]     26.44   12.03  17.01   3.25  12.93  19.96   48.2  52.47    2.0   3.26\n",
      "un[22]      25.9   12.43  17.58   3.06  12.41  17.21  49.04   52.8    2.0   3.46\n",
      "un[23]     26.61   12.03  17.01   3.57  12.98  20.35  48.96  52.24    2.0   3.32\n",
      "un[24]     25.91   12.56  17.76   3.44  10.99  17.88  49.42  52.48    2.0   3.41\n",
      "un[25]     25.95   11.94  16.88   3.27  12.98  18.83  48.36   51.4    2.0   3.38\n",
      "un[26]     25.83   11.99  16.95   3.38  12.88  17.91  48.54  50.98    2.0    3.5\n",
      "un[27]     26.31   12.54  17.73   3.23  12.38  17.75  49.81  52.82    2.0   3.46\n",
      "un[28]     26.16   11.86  16.77   3.34  13.31  18.57  47.57  51.16    2.0   3.02\n",
      "un[29]     26.31   12.74  18.02   3.39  12.34  17.63  49.86  53.94    2.0   3.61\n",
      "un[30]     25.74   12.27  17.36   3.25  12.77  17.26  49.13   51.5    2.0   3.61\n",
      "un[31]      25.9   11.94  16.89   3.26  13.07  17.81  47.87   51.8    2.0   3.37\n",
      "un[32]     26.04   12.34  17.45   3.34  13.03  17.89  49.12   52.3    2.0    3.5\n",
      "un[33]     26.13   11.97  16.93   3.31  13.05  18.76  48.64  51.53    2.0   3.48\n",
      "un[34]     25.66   12.15  17.18   3.35  12.34  17.86  48.18  51.69    2.0   3.51\n",
      "un[35]     26.21   12.23  17.29   3.41  12.99  18.34  49.24  52.11    2.0   3.48\n",
      "un[36]     25.56   12.01  16.99   3.31  11.55  17.96  47.66  51.12    2.0   3.41\n",
      "un[37]     26.12   11.95  16.91   3.61   13.0  19.15  48.46  51.29    2.0   3.26\n",
      "un[38]     26.14    12.5  17.68   3.39  12.23  17.94  49.36  52.76    2.0   3.46\n",
      "un[39]     26.17   11.92  16.85    3.3  13.11  19.16  48.28  51.38    2.0   3.38\n",
      "un[40]     25.96   12.09   17.1   3.42  12.28  18.44  48.65  51.33    2.0   3.34\n",
      "un[41]     25.74   12.12  17.15   3.14  12.57  17.15  48.32  51.73    2.0   3.35\n",
      "un[42]     26.28   12.16  17.19   3.42  12.98  18.79  48.93  51.84    2.0   3.37\n",
      "un[43]     26.03    12.0  16.97   3.15  12.76  18.58  47.93  51.87    2.0   3.32\n",
      "un[44]     25.53   12.16   17.2   3.36  11.62  17.94  48.42   51.3    2.0   3.48\n",
      "un[45]     26.11   12.11  17.13    3.4  12.58  18.59   48.6  51.95    2.0   3.38\n",
      "un[46]      25.9   12.18  17.23   3.36  12.98  17.87  48.23  52.63    2.0   3.45\n",
      "un[47]     26.66   12.47  17.64   3.22  12.68  18.76  49.44  53.28    2.0   3.15\n",
      "un[48]     25.86   11.98  16.94   3.31  12.72  17.39  48.08  51.29    2.0   3.26\n",
      "un[49]     26.38   12.24  17.31   3.34  13.21  18.13   49.2  52.37    2.0   3.29\n",
      "un[50]     25.66   12.18  17.22   3.24  12.55  17.69   48.4   51.7    2.0   3.55\n",
      "un[51]     26.08   12.21  17.27   3.23  12.86  18.27  48.97  51.86    2.0   3.48\n",
      "un[52]     26.04   12.22  17.28   3.35  12.77  17.32  48.95  51.73    2.0    3.4\n",
      "un[53]     26.46   12.26  17.33   3.39  12.87  19.48  49.49  52.22    2.0   3.32\n",
      "un[54]     25.51    12.2  17.26   2.99  11.64  17.55   48.2  50.94    2.0   3.45\n",
      "un[55]     26.24   11.93  16.87   3.27  13.19  19.84  48.29  51.62    2.0   3.23\n",
      "un[56]      25.4   12.18  17.22   3.42   11.4  17.66  48.02  51.15    2.0   3.45\n",
      "un[57]     26.53   12.07  17.07   3.41  13.31  20.12  48.92  52.15    2.0    3.3\n",
      "un[58]     25.43   12.28  17.36   3.35  11.72  17.17  48.46  51.51    2.0   3.59\n",
      "un[59]     26.06   12.29  17.38   3.37  13.18  17.72  49.53  51.69    2.0    3.5\n",
      "un[60]     26.17   11.77  16.65   3.39  13.32  19.45  48.21  50.52    2.0   3.37\n",
      "un[61]     25.62   12.41  17.55   3.72   11.6  17.56  48.93  51.45    2.0   3.18\n",
      "un[62]      26.4   12.03  17.01   3.41  12.92  19.58  47.97  52.58    2.0   3.33\n",
      "un[63]     25.56   12.31  17.41   3.55  11.66  17.58   48.2  52.23    2.0   3.45\n",
      "un[64]     25.94   11.87  16.79   3.72  13.02  18.19  47.54  51.45    2.0   3.29\n",
      "un[65]     26.13   12.32  17.43   3.73  13.02  17.81  48.84  53.12    2.0   3.64\n",
      "un[66]     25.75   12.08  17.09   3.14  12.58  17.52  48.41  51.36    2.0    3.5\n",
      "un[67]     26.12   12.17  17.22   3.49  12.81   18.7  49.28  51.61    2.0    3.5\n",
      "un[68]     25.85   12.07  17.07   3.32  12.77   18.1  47.77  52.07    2.0    3.3\n",
      "un[69]     25.81   12.06  17.06   3.31  12.54  17.99  48.48  51.42    2.0   3.47\n",
      "un[70]     26.05   12.15  17.18   3.28  12.87  18.69  48.68  51.93    2.0   3.47\n",
      "un[71]     25.96   12.34  17.45   3.58  12.42  17.92  48.99  52.18    2.0    3.4\n",
      "un[72]     25.72   12.03  17.01   3.31  12.49  18.42  48.27  51.35    2.0   3.56\n",
      "un[73]     26.47   12.26  17.33   3.55  12.31  19.17  48.77  52.59    2.0   3.11\n",
      "un[74]     25.69   12.11  17.13   3.56  12.08  18.39   48.3  51.86    2.0   3.57\n",
      "un[75]     26.04   12.19  17.23   3.76  11.85  18.54  48.89  51.88    2.0   3.46\n",
      "un[76]     25.49   11.88   16.8   3.85  12.16  18.68  47.16  50.68    2.0   3.44\n",
      "un[77]     26.49   12.03  17.02   3.84  13.25  20.05  48.62  51.86    2.0   3.11\n",
      "un[78]     25.36   12.37  17.49   3.65   11.1   17.5  48.93  51.41    2.0    3.5\n",
      "un[79]     27.02   11.97  16.92   3.75  13.18  21.54  48.92  52.41    2.0   3.13\n",
      "un[80]      25.3   12.62  17.85   3.52   9.65  17.82  49.16  51.83    2.0   3.38\n",
      "un[81]     26.27   11.98  16.95   3.47  13.01  19.53  48.19  51.99    2.0   3.26\n",
      "un[82]     25.92   12.36  17.48   3.46  12.09  17.79  48.96  52.67    2.0   3.46\n",
      "un[83]     26.26    11.7  16.55   3.33  13.44  19.44  48.06  50.57    2.0   3.17\n",
      "un[84]     25.86    12.6  17.82   3.83  11.51  17.79  49.69  52.88    2.0   3.53\n",
      "un[85]     26.08   11.74   16.6   3.82  12.98  19.56  47.58  50.89    2.0   3.24\n",
      "un[86]     25.92   12.59   17.8   3.56   11.4   18.3   49.8  52.78    2.0    3.6\n",
      "un[87]     26.48   11.85  16.76   3.34  12.92  20.28  47.89  51.67    2.0   3.08\n",
      "un[88]     25.32   12.34  17.45   3.33  11.35  17.07  48.67   51.6    2.0   3.61\n",
      "un[89]     26.38   11.92  16.85   3.22  13.06  19.63   48.1  51.94    2.0   3.26\n",
      "un[90]     25.47   12.06  17.05   3.35   11.6  17.95   47.8  51.27    2.0   3.46\n",
      "un[91]     26.86   12.35  17.47   3.31   12.8  19.69  49.56  52.88    2.0    3.1\n",
      "un[92]     25.73   12.29  17.38   3.32  12.41  17.38  48.84  51.89    2.0   3.48\n",
      "un[93]     26.79   11.94  16.88   3.32  13.36  20.83  48.84  51.49    2.0   3.06\n",
      "un[94]     25.04    12.2  17.26   3.21  11.25  17.06  47.71  51.42    2.0   3.65\n",
      "un[95]     26.17   11.88   16.8   3.32  13.27   18.9  48.02  51.46    2.0   3.32\n",
      "un[96]     25.78   12.19  17.24   3.06  12.54  17.29  48.85  51.46    2.0   3.56\n",
      "un[97]     26.35   12.35  17.47   3.08  12.67  19.32  49.25  53.09    2.0   3.39\n",
      "un[98]     25.79   12.37  17.49   3.29  12.57  17.11  49.25  52.03    2.0   3.58\n",
      "un[99]     26.29   12.49  17.66   3.35  11.87  18.54  49.56  52.92    2.0    3.5\n",
      "un[100]    25.36   11.88  16.81   3.24  11.54  17.87  47.52  50.81    2.0   3.43\n",
      "un[101]    26.35   12.17  17.21   3.29   12.9  18.52  48.87  52.05    2.0   3.29\n",
      "un[102]     26.1   12.09   17.1   3.29  13.15  18.17  48.41  52.67    2.0   3.52\n",
      "un[103]    25.96   11.94  16.89   3.13   13.1  18.17   48.2  51.32    2.0   3.44\n",
      "un[104]    26.52   12.69  17.94   3.34  12.74  17.61  50.64  53.38    2.0   3.55\n",
      "un[105]    25.62   11.55  16.33    3.4  12.72  18.97  46.48  50.87    2.0   3.37\n",
      "un[106]    26.14   12.84  18.16   3.28  11.37  18.31  50.27  53.63    2.0   3.55\n",
      "un[107]    25.57   11.52  16.29   3.26  12.55  18.83  46.34  50.12    2.0   3.14\n",
      "un[108]    25.96   12.34  17.46    3.3   12.2  18.13  49.42  51.89    2.0   3.58\n",
      "un[109]    25.96   12.31  17.41   3.32   11.6  18.66  48.71  52.36    2.0   3.53\n",
      "un[110]    25.76   12.14  17.17   3.12  11.61  18.35  48.49  51.51    2.0   3.36\n",
      "un[111]    26.29   12.44  17.59   3.31  12.44   18.8  49.63  52.88    2.0   3.53\n",
      "un[112]    26.23   12.31  17.41   3.32  12.92  18.33  49.55  52.25    2.0   3.56\n",
      "un[113]    25.94    12.1  17.11    3.3   11.9  18.46  48.37  51.57    2.0   3.24\n",
      "un[114]    26.11   12.39  17.53    3.6  12.53  18.38  49.25   52.6    2.0   3.57\n",
      "un[115]    25.96    12.1  17.11   3.59   12.4  17.67  48.41  51.51    2.0   3.27\n",
      "un[116]    26.53   12.22  17.28   2.82  12.95  19.75  49.12  52.49    2.0    3.3\n",
      "un[117]    25.62    12.2  17.25   3.33  11.53  17.43  48.72  51.28    2.0   3.49\n",
      "un[118]    26.56   11.96  16.91    3.2  12.91  20.65  48.47  51.59    2.0   3.06\n",
      "un[119]    25.81   12.44   17.6   3.36  11.29  18.47  48.89   52.7    2.0   3.55\n",
      "un[120]    25.89   12.01  16.99   3.59  11.77  19.17  47.74  51.72    2.0   3.33\n",
      "un[121]    26.36   12.36  17.47   3.73  11.92  18.81  49.03  52.68    2.0   3.32\n",
      "un[122]    26.36   12.39  17.52   3.74  12.42  18.32  49.51  52.52    2.0   3.43\n",
      "un[123]    26.21   12.29  17.38   3.03  12.89  18.61  49.23  52.52    2.0   3.53\n",
      "un[124]    26.23   12.31   17.4   3.35  12.46  18.27  49.37  52.27    2.0   3.36\n",
      "un[125]    26.08   12.08  17.08   3.25  12.97  18.68  48.48  51.67    2.0   3.43\n",
      "un[126]    25.45   11.97  16.92   3.44   12.5  17.02  47.48   51.6    2.0    3.5\n",
      "un[127]    26.17   11.88   16.8   3.68  12.91  18.97  48.47  50.91    2.0    3.3\n",
      "un[128]    26.11   12.83  18.14   3.15  11.72  17.63  50.17  53.85    2.0   3.64\n",
      "un[129]    26.13    11.9  16.83   3.74  12.11  20.74   47.1  52.62    2.0   3.31\n",
      "un[130]    26.01   12.67  17.91   3.72  10.93  18.32  48.46  54.45    2.0   3.56\n",
      "un[131]    26.26   12.02   17.0   3.65  13.08  19.74  48.15  52.34    2.0   3.39\n",
      "un[132]    26.11   12.45   17.6   3.82  13.05  17.48  49.45  53.53    2.0   3.68\n",
      "un[133]    26.11   11.68  16.51   3.43  13.29   19.8  47.02  51.31    2.0   3.19\n",
      "un[134]    25.33   12.39  17.53   3.26  11.15  16.61  48.35  52.67    2.0   3.68\n",
      "un[135]    26.48    12.0  16.97   3.72  13.07  19.75  48.61  51.82    2.0   3.27\n",
      "un[136]    25.83   12.56  17.76   3.75  11.35  17.77  49.28  52.76    2.0   3.72\n",
      "un[137]     26.4   11.85  16.76   3.79  12.31  20.77  47.92  51.19    2.0   3.07\n",
      "un[138]     25.4   12.19  17.24   3.46  11.74  16.95  48.44  51.44    2.0   3.63\n",
      "un[139]     26.6   11.93  16.87   3.16  13.18  20.13   48.6  51.24    2.0   3.07\n",
      "un[140]     25.8   12.18  17.22   3.25  11.97  18.01  48.75  51.36    2.0   3.43\n",
      "un[141]    26.14   12.12  17.14   3.33  12.48  18.19  48.73  51.47    2.0   3.38\n",
      "un[142]    25.42   12.01  16.99   3.38  11.89  17.49  47.73  51.05    2.0   3.51\n",
      "un[143]    26.96    12.2  17.26   3.26  13.28  20.19  49.32  52.96    2.0   3.17\n",
      "un[144]    25.14    12.2  17.25   3.23  10.81  17.65  48.09  50.92    2.0   3.51\n",
      "un[145]    26.69   12.15  17.19   3.44   13.1  20.51   49.3  52.12    2.0   3.28\n",
      "un[146]    25.39   12.35  17.46   3.35  10.86  17.34  48.28  51.86    2.0   3.48\n",
      "un[147]    25.88   12.15  17.18    3.3   12.3  18.17  48.26  51.68    2.0   3.48\n",
      "un[148]    26.07   12.25  17.32    3.3   12.7  18.43  48.97  52.15    2.0   3.47\n",
      "un[149]    26.43   12.41  17.56   3.42  13.06  18.01  49.94   53.2    2.0   3.62\n",
      "un[150]    26.14   12.05  17.05   3.28  12.69  19.58  48.38   51.9    2.0   3.21\n",
      "un[151]    26.14    12.3  17.39    3.4  11.87  19.83  49.14  52.19    2.0   3.36\n",
      "un[152]    25.54   12.28  17.37   3.35  11.66  17.57  48.68  51.33    2.0   3.46\n",
      "un[153]    25.81   12.22  17.29   3.24  11.77  18.14  48.97   51.6    2.0   3.57\n",
      "un[154]    26.06    12.2  17.26   3.26   12.9  17.37  49.04  51.36    2.0   3.25\n",
      "un[155]    25.97   12.54  17.74   3.29  12.29  17.32  49.91  52.63    2.0   3.69\n",
      "un[156]    26.02   12.11  17.13   3.35  12.95  17.71  48.49  51.74    2.0   3.34\n",
      "un[157]    26.14   12.26  17.35   3.42  12.95  18.15  48.88  52.59    2.0    3.5\n",
      "un[158]    26.32   12.35  17.46   3.44   13.1  17.78  49.14  52.73    2.0   3.31\n",
      "un[159]    25.89   11.83  16.73    3.3   13.0  18.65  47.75  51.23    2.0    3.4\n",
      "un[160]    26.19   12.55  17.75   3.26  12.64  17.77  49.83  52.87    2.0   3.65\n",
      "un[161]    25.97   11.87  16.78    3.4  13.09   18.5  47.36  51.76    2.0   3.36\n",
      "un[162]    26.15   12.42  17.56    3.3  12.21  18.28  49.39  52.65    2.0   3.52\n",
      "un[163]    26.27    12.4  17.53   3.38  12.91  18.01  49.61  52.49    2.0   3.53\n",
      "un[164]    25.81   12.11  17.13   3.36  12.17  17.82  48.72   51.1    2.0   3.36\n",
      "un[165]    26.31    12.2  17.25   3.35  12.96  20.04  49.01   52.4    2.0   3.38\n",
      "un[166]    25.72   12.17  17.21   3.42  11.76  18.31  47.42  51.92    2.0   3.28\n",
      "un[167]    26.01   12.18  17.23   3.26  12.74  18.61  49.03  51.68    2.0   3.58\n",
      "un[168]    25.94   12.11  17.13    3.2  11.92  18.47  48.32  51.62    2.0   3.23\n",
      "un[169]    25.98    12.2  17.25   3.21  12.95  17.37  49.16  51.97    2.0    3.6\n",
      "un[170]    25.96   12.15  17.18   3.21  12.79  17.56   48.7  51.81    2.0    3.4\n",
      "un[171]    25.93   12.33  17.43   3.34  12.37  17.41  48.72  52.92    2.0   3.63\n",
      "un[172]    25.86    12.2  17.26   3.24  12.82  17.46  48.75   51.9    2.0   3.45\n",
      "un[173]    26.58   12.29  17.38   3.33  13.47  18.86   49.2  52.84    2.0   3.46\n",
      "un[174]    25.39   12.22  17.29   3.18  11.56  16.73  48.44   51.2    2.0   3.58\n",
      "un[175]    26.35    12.4  17.53   3.39  11.82  19.33  49.87  52.34    2.0   3.38\n",
      "un[176]    25.37   11.93  16.88   3.19  12.23  17.96  47.73  50.51    2.0   3.51\n",
      "beta[0]      1.0    0.03   0.05   0.93   0.94   1.02   1.04   1.06    2.0   2.57\n",
      "beta[1]     0.94    0.03   0.04   0.88   0.89   0.95   0.96   0.97    2.0   3.36\n",
      "beta[2]     0.99    0.03   0.04   0.93   0.93   1.01   1.02   1.03    2.0   3.72\n",
      "beta[3]     0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.73\n",
      "beta[4]     0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0    3.8\n",
      "beta[5]     0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.78\n",
      "beta[6]     0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0    3.7\n",
      "beta[7]     0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.64\n",
      "beta[8]     0.97    0.03   0.04   0.91   0.91   0.99   0.99    1.0    2.0   3.76\n",
      "beta[9]     0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.57\n",
      "beta[10]    0.94    0.03   0.04   0.88   0.88   0.96   0.96   0.97    2.0    3.9\n",
      "beta[11]    1.07    0.03   0.04    1.0   1.01   1.09    1.1   1.11    2.0   3.47\n",
      "beta[12]    0.84    0.02   0.03   0.78   0.79   0.86   0.86   0.87    2.0    3.8\n",
      "beta[13]    1.03    0.03   0.04   0.97   0.98   1.05   1.06   1.07    2.0   3.63\n",
      "beta[14]    1.01    0.03   0.04   0.95   0.96   1.04   1.05   1.05    2.0    3.9\n",
      "beta[15]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.64\n",
      "beta[16]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.81\n",
      "beta[17]    0.91    0.03   0.04   0.85   0.86   0.93   0.94   0.95    2.0    3.7\n",
      "beta[18]    1.05    0.03   0.04   0.98   0.99   1.07   1.08   1.08    2.0   4.06\n",
      "beta[19]    0.93    0.03   0.04   0.87   0.88   0.95   0.96   0.97    2.0   3.77\n",
      "beta[20]    0.99    0.03   0.04   0.93   0.94   1.01   1.01   1.02    2.0   3.91\n",
      "beta[21]    0.98    0.03   0.04   0.91   0.93    1.0   1.01   1.02    2.0   3.84\n",
      "beta[22]    0.95    0.03   0.04   0.88   0.89   0.97   0.97   0.98    2.0   3.81\n",
      "beta[23]    0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0    3.6\n",
      "beta[24]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   4.03\n",
      "beta[25]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.59\n",
      "beta[26]    0.96    0.03   0.04   0.91   0.91   0.98   0.99    1.0    2.0   3.73\n",
      "beta[27]    0.96    0.03   0.04    0.9   0.91   0.99   0.99    1.0    2.0   3.98\n",
      "beta[28]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.77\n",
      "beta[29]    0.97    0.03   0.04   0.91   0.92    1.0   1.01   1.01    2.0   4.01\n",
      "beta[30]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.84\n",
      "beta[31]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.01    2.0   3.89\n",
      "beta[32]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.93\n",
      "beta[33]    0.96    0.03   0.04    0.9    0.9   0.98   0.99   0.99    2.0   3.74\n",
      "beta[34]    0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   3.79\n",
      "beta[35]    0.94    0.03   0.04   0.88   0.89   0.97   0.97   0.98    2.0   3.87\n",
      "beta[36]    0.98    0.03   0.04   0.93   0.93   1.01   1.01   1.02    2.0   3.74\n",
      "beta[37]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.81\n",
      "beta[38]    0.98    0.03   0.04   0.92   0.92    1.0   1.01   1.02    2.0   3.87\n",
      "beta[39]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.63\n",
      "beta[40]    0.97    0.03   0.04   0.91   0.91   0.99   0.99    1.0    2.0   3.81\n",
      "beta[41]    0.97    0.03   0.04   0.91   0.92   0.99    1.0    1.0    2.0   3.82\n",
      "beta[42]    0.96    0.03   0.04   0.91   0.91   0.98   0.99    1.0    2.0   3.77\n",
      "beta[43]    0.99    0.03   0.04   0.92   0.94   1.01   1.02   1.02    2.0   3.85\n",
      "beta[44]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.77\n",
      "beta[45]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.77\n",
      "beta[46]    0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0    3.7\n",
      "beta[47]    0.96    0.03   0.04   0.89    0.9   0.98   0.99    1.0    2.0   4.01\n",
      "beta[48]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.01    2.0   3.84\n",
      "beta[49]    0.96    0.03   0.04    0.9   0.91   0.99   0.99    1.0    2.0    3.8\n",
      "beta[50]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.96\n",
      "beta[51]    0.97    0.03   0.04   0.91   0.92   0.99    1.0    1.0    2.0   3.87\n",
      "beta[52]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.91\n",
      "beta[53]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.91\n",
      "beta[54]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0    4.0\n",
      "beta[55]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.74\n",
      "beta[56]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0    4.1\n",
      "beta[57]    0.97    0.03   0.04   0.91   0.91   0.99    1.0    1.0    2.0   3.68\n",
      "beta[58]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   4.02\n",
      "beta[59]    0.96    0.03   0.04    0.9    0.9   0.98   0.99   0.99    2.0   3.86\n",
      "beta[60]    0.98    0.03   0.04   0.93   0.93    1.0   1.01   1.02    2.0   3.68\n",
      "beta[61]    0.96    0.03   0.04    0.9   0.91   0.98   0.99   0.99    2.0   3.95\n",
      "beta[62]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.52\n",
      "beta[63]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.93\n",
      "beta[64]    0.98    0.03   0.04   0.92   0.93    1.0    1.0   1.01    2.0   3.65\n",
      "beta[65]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.97\n",
      "beta[66]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.82\n",
      "beta[67]    0.98    0.03   0.04   0.92   0.92    1.0   1.01   1.01    2.0    3.9\n",
      "beta[68]    0.97    0.03   0.04   0.91   0.91   0.99   0.99    1.0    2.0   3.77\n",
      "beta[69]    0.97    0.03   0.04    0.9   0.92   0.99   0.99    1.0    2.0   3.91\n",
      "beta[70]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.81\n",
      "beta[71]    0.97    0.03   0.04   0.91   0.91   0.99    1.0   1.01    2.0   3.95\n",
      "beta[72]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.77\n",
      "beta[73]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.89\n",
      "beta[74]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.75\n",
      "beta[75]    0.98    0.03   0.04   0.92   0.92    1.0   1.01   1.02    2.0   3.93\n",
      "beta[76]    0.96    0.03   0.04   0.91   0.91   0.99   0.99    1.0    2.0   3.64\n",
      "beta[77]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.73\n",
      "beta[78]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.99\n",
      "beta[79]    0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   3.48\n",
      "beta[80]    0.95    0.03   0.04   0.89    0.9   0.98   0.98   0.99    2.0   4.01\n",
      "beta[81]    0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.66\n",
      "beta[82]    0.97    0.03   0.04    0.9   0.92   0.99    1.0   1.01    2.0   3.82\n",
      "beta[83]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.61\n",
      "beta[84]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   4.11\n",
      "beta[85]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.51\n",
      "beta[86]    0.98    0.03   0.04   0.91   0.92    1.0   1.01   1.02    2.0   3.96\n",
      "beta[87]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.61\n",
      "beta[88]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.83\n",
      "beta[89]    0.96    0.03   0.04   0.91   0.91   0.98   0.99    1.0    2.0   3.55\n",
      "beta[90]    0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.91\n",
      "beta[91]    0.96    0.03   0.04    0.9   0.91   0.99    1.0    1.0    2.0   3.78\n",
      "beta[92]    0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.83\n",
      "beta[93]    1.01    0.03   0.04   0.95   0.96   1.03   1.05   1.05    2.0   3.56\n",
      "beta[94]    0.92    0.03   0.04   0.86   0.87   0.94   0.95   0.95    2.0   4.12\n",
      "beta[95]    0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   3.73\n",
      "beta[96]    0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.95\n",
      "beta[97]    0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   4.01\n",
      "beta[98]    0.94    0.03   0.04   0.88   0.89   0.96   0.97   0.98    2.0   3.96\n",
      "beta[99]    0.98    0.03   0.04   0.92   0.93   1.01   1.02   1.02    2.0   3.81\n",
      "beta[100]   0.96    0.03   0.04    0.9   0.91   0.98   0.98   0.99    2.0   3.87\n",
      "beta[101]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.82\n",
      "beta[102]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.74\n",
      "beta[103]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.87\n",
      "beta[104]   0.99    0.03   0.04   0.93   0.93   1.01   1.02   1.03    2.0   3.88\n",
      "beta[105]   0.96    0.02   0.03    0.9   0.91   0.98   0.99   0.99    2.0   3.81\n",
      "beta[106]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.89\n",
      "beta[107]   0.96    0.02   0.04   0.91   0.92   0.98   0.99    1.0    2.0   3.69\n",
      "beta[108]   0.99    0.03   0.04   0.93   0.93   1.01   1.02   1.02    2.0   3.86\n",
      "beta[109]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.98\n",
      "beta[110]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.96\n",
      "beta[111]   0.98    0.03   0.04   0.91   0.92    1.0   1.01   1.02    2.0   3.79\n",
      "beta[112]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.92\n",
      "beta[113]   0.96    0.03   0.04    0.9   0.91   0.98   0.99   0.99    2.0   4.02\n",
      "beta[114]   0.98    0.03   0.04   0.92   0.92    1.0   1.01   1.01    2.0   3.86\n",
      "beta[115]   0.97    0.03   0.04   0.91   0.92   0.99   0.99    1.0    2.0   4.01\n",
      "beta[116]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.88\n",
      "beta[117]   0.97    0.03   0.04   0.91   0.91   0.99   0.99    1.0    2.0   3.98\n",
      "beta[118]   0.98    0.03   0.04   0.92   0.92    1.0   1.01   1.01    2.0   3.78\n",
      "beta[119]   0.96    0.03   0.04    0.9   0.91   0.98   0.99   0.99    2.0   4.03\n",
      "beta[120]   0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.02    2.0   3.77\n",
      "beta[121]   0.95    0.03   0.04   0.89    0.9   0.98   0.98   0.99    2.0   3.82\n",
      "beta[122]   0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   3.99\n",
      "beta[123]   0.95    0.03   0.04    0.9    0.9   0.97   0.98   0.99    2.0   4.02\n",
      "beta[124]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.87\n",
      "beta[125]   0.96    0.03   0.04   0.91   0.91   0.98   0.99    1.0    2.0    4.0\n",
      "beta[126]   0.96    0.03   0.04    0.9   0.92   0.98   0.99    1.0    2.0   3.82\n",
      "beta[127]   0.96    0.02   0.04    0.9   0.92   0.98   0.99    1.0    2.0   3.83\n",
      "beta[128]   0.98    0.03   0.04   0.92   0.93   1.01   1.01   1.02    2.0   4.17\n",
      "beta[129]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0    3.6\n",
      "beta[130]   0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   4.19\n",
      "beta[131]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.66\n",
      "beta[132]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   4.04\n",
      "beta[133]   0.97    0.02   0.04   0.92   0.93   0.99    1.0   1.01    2.0   3.52\n",
      "beta[134]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   4.07\n",
      "beta[135]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.73\n",
      "beta[136]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   4.34\n",
      "beta[137]   0.97    0.03   0.04   0.92   0.93   0.99    1.0   1.01    2.0   3.69\n",
      "beta[138]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.94\n",
      "beta[139]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.73\n",
      "beta[140]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.99\n",
      "beta[141]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.85\n",
      "beta[142]   0.96    0.03   0.04    0.9   0.91   0.98   0.98   0.99    2.0   3.95\n",
      "beta[143]   0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   3.74\n",
      "beta[144]   0.95    0.03   0.04   0.89    0.9   0.97   0.97   0.98    2.0   4.04\n",
      "beta[145]   0.99    0.03   0.04   0.93   0.94   1.01   1.02   1.03    2.0   3.68\n",
      "beta[146]   0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   4.04\n",
      "beta[147]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.89\n",
      "beta[148]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.88\n",
      "beta[149]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.88\n",
      "beta[150]   0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.93\n",
      "beta[151]   0.99    0.03   0.04   0.93   0.94   1.02   1.02   1.03    2.0   3.67\n",
      "beta[152]   0.96    0.03   0.04    0.9    0.9   0.98   0.98   0.99    2.0   3.94\n",
      "beta[153]   0.96    0.03   0.04    0.9   0.91   0.98   0.99   0.99    2.0    4.0\n",
      "beta[154]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0    4.0\n",
      "beta[155]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   4.05\n",
      "beta[156]   0.98    0.03   0.04   0.92   0.93    1.0    1.0   1.01    2.0    3.9\n",
      "beta[157]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.01    2.0   3.97\n",
      "beta[158]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.97\n",
      "beta[159]   0.96    0.03   0.04    0.9   0.91   0.98   0.99   0.99    2.0   3.71\n",
      "beta[160]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0    4.0\n",
      "beta[161]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0    3.7\n",
      "beta[162]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.93\n",
      "beta[163]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.99\n",
      "beta[164]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.01    2.0   4.07\n",
      "beta[165]   0.96    0.03   0.04   0.91   0.91   0.98   0.99    1.0    2.0   3.76\n",
      "beta[166]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0    4.0\n",
      "beta[167]   0.96    0.03   0.04    0.9   0.91   0.98   0.99   0.99    2.0   3.81\n",
      "beta[168]   0.98    0.03   0.04   0.92   0.92    1.0    1.0   1.01    2.0   3.87\n",
      "beta[169]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.91\n",
      "beta[170]   0.95    0.03   0.04   0.89    0.9   0.97   0.98   0.99    2.0   3.89\n",
      "beta[171]   0.98    0.03   0.04   0.91   0.93    1.0   1.01   1.01    2.0   3.99\n",
      "beta[172]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.76\n",
      "beta[173]   0.98    0.03   0.04   0.92   0.93    1.0   1.01   1.02    2.0   3.81\n",
      "beta[174]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   4.03\n",
      "beta[175]   0.97    0.03   0.04   0.91   0.92   0.99    1.0   1.01    2.0   3.92\n",
      "beta[176]   0.96    0.03   0.04    0.9   0.91   0.98   0.99    1.0    2.0   3.67\n",
      "beta[177]   3.97    5.76  11.51  -18.6  -6.18   6.86  14.62   18.2    4.0   1.27\n",
      "uc[0]       0.06  3.5e-3   0.02 8.0e-3   0.04   0.06   0.07    0.1   49.0   1.06\n",
      "uc[1]       0.99  7.9e-4 6.9e-3   0.97   0.98   0.99   0.99    1.0   77.0   1.04\n",
      "unc[0]     32.33   15.11  21.37   2.88  14.24  26.55  59.97  61.42    2.0   2.88\n",
      "unc[1]     -0.23    0.04   0.33  -0.79  -0.44  -0.21  -0.05   0.55   57.0   1.02\n",
      "b[0]         2.2    0.13   0.18   1.94    2.0   2.23   2.35   2.52    2.0   1.65\n",
      "b[1]       -0.91  6.8e-3   0.06  -1.07  -0.95  -0.91  -0.88   -0.8   90.0   1.03\n",
      "b[2]       -0.35    0.05   0.08  -0.51  -0.42  -0.35  -0.28  -0.24    3.0   1.35\n",
      "sb          0.01  1.0e-4 8.1e-4   0.01   0.01   0.02   0.02   0.02   61.0    nan\n",
      "su          0.13  2.3e-3 6.9e-3   0.12   0.12   0.13   0.13   0.14    9.0   1.08\n",
      "sun         1.97    0.19    1.3   0.36   1.27   1.59   2.46   5.73   48.0   1.04\n",
      "s         3.1e-3  3.8e-4 1.0e-3 1.4e-3 2.3e-3 3.1e-3 3.7e-3 5.1e-3    7.0    nan\n",
      "lp__      2628.3   84.64  146.6 2353.0 2526.7 2631.2 2769.2 2816.3    3.0   1.45\n",
      "\n",
      "Samples were drawn using NUTS(diag_e) at Sun Sep 25 03:04:21 2016.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "fit_ubv,res_ubv=runmodel(model_ubv,data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# フル状態空間モデル"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_fullstate=\"\"\"\n",
    "data{\n",
    "        int<lower=1> N;\n",
    "        real<lower=0,upper=100> pi[N];\n",
    "        real<lower=0,upper=100> u[N];\n",
    "}\n",
    "\n",
    "parameters{\n",
    "    real L[N]; //lambda*phi    \n",
    "    real<lower=0> un; //natural unemployment rate\n",
    "    real<lower=-20,upper=20> beta[N];    \n",
    "    real uc[3];\n",
    "    real cL[3];\n",
    "    real b[3];\n",
    "    real<lower=0> sb;\n",
    "    real<lower=0> su;\n",
    "    real<lower=0> sL;\n",
    "    real<lower=0> s;\n",
    "}\n",
    "\n",
    "model{\n",
    "    real p[N-1];\n",
    "    \n",
    "    for(i in 3:(N)){\n",
    "        u[i]~normal(uc[1]+uc[2]*u[i-1]+uc[3]*u[i-2],su);\n",
    "        L[i]~normal(cL[1]+cL[2]*L[i-1]+cL[3]*L[i-2],sL);\n",
    "        beta[i]~normal(b[1]+b[2]*beta[i-1]+b[3]*beta[i-2],sb);\n",
    "    }    \n",
    "\n",
    "    for(i in 1:(N-1))\n",
    "        p[i]<-beta[i]*pi[i+1]-L[i]*(u[i]-un);\n",
    "        \n",
    "    for(i in 1:(N-1))\n",
    "        pi[i]~normal(p[i],s);\n",
    "}         \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_69c30c64c53f48772822e6f69024553f.\n",
      "3 chains, each with iter=31000; warmup=1000; thin=10; \n",
      "post-warmup draws per chain=3000, total post-warmup draws=9000.\n",
      "\n",
      "            mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "L[0]       -0.12    0.05    0.9  -2.31  -0.26  -0.02    0.1   1.42  271.0   1.01\n",
      "L[1]        0.09    0.02   0.33  -0.47  -0.04   0.01   0.17   0.92  271.0   1.01\n",
      "L[2]       -0.05  7.6e-3   0.14  -0.41  -0.09  -0.01   0.02   0.19  330.0   1.02\n",
      "L[3]        0.02  4.8e-3    0.1  -0.16  -0.02 1.6e-3   0.04   0.28  454.0   1.01\n",
      "L[4]     -4.1e-3  3.8e-3   0.07  -0.17  -0.02-3.1e-4   0.02   0.14  358.0    1.0\n",
      "L[5]      2.8e-3  2.4e-3   0.04   -0.1-9.3e-3 4.2e-3   0.02   0.09  309.0    1.0\n",
      "L[6]     -1.6e-3  1.3e-3   0.02  -0.04  -0.01-3.5e-3 5.4e-3   0.05  322.0    1.0\n",
      "L[7]     -3.7e-3  5.4e-4   0.01  -0.03-8.5e-3-2.7e-3 2.0e-3   0.02  548.0    1.0\n",
      "L[8]      5.8e-4  2.8e-4 8.1e-3  -0.01-3.3e-3 4.0e-4 4.2e-3   0.02  821.0    1.0\n",
      "L[9]     -7.4e-4  2.5e-4 6.7e-3  -0.01-4.3e-3-6.6e-4 2.7e-3   0.01  702.0    1.0\n",
      "L[10]    -9.9e-3  2.3e-4 5.8e-3  -0.02  -0.01  -0.01-6.6e-3 2.4e-3  610.0    1.0\n",
      "L[11]       0.03  1.8e-4 4.8e-3   0.02   0.02   0.03   0.03   0.04  718.0    1.0\n",
      "L[12]      -0.05  1.9e-4 5.9e-3  -0.06  -0.05  -0.05  -0.04  -0.03  960.0    1.0\n",
      "L[13]       0.02  1.5e-4 4.7e-3 9.5e-3   0.02   0.02   0.02   0.03  994.0    1.0\n",
      "L[14]       0.02  1.3e-4 4.4e-3   0.01   0.02   0.02   0.02   0.03 1168.0    1.0\n",
      "L[15]     1.2e-3  1.0e-4 4.1e-3-7.0e-3-1.4e-3 1.2e-3 3.9e-3 9.1e-3 1626.0    1.0\n",
      "L[16]     1.1e-4  1.5e-4 4.8e-3-9.9e-3-2.7e-3 3.1e-4 3.2e-3 8.9e-3  961.0    1.0\n",
      "L[17]      -0.02  1.8e-4 5.5e-3  -0.03  -0.02  -0.02  -0.02-9.7e-3  961.0    1.0\n",
      "L[18]       0.03  1.7e-4 5.5e-3   0.02   0.02   0.03   0.03   0.04 1028.0    1.0\n",
      "L[19]      -0.02  1.5e-4 5.5e-3  -0.03  -0.02  -0.02  -0.01-5.1e-3 1289.0    1.0\n",
      "L[20]     6.2e-3  1.2e-4 4.9e-3-3.5e-3 3.0e-3 6.2e-3 9.4e-3   0.02 1670.0    1.0\n",
      "L[21]     4.3e-3  1.1e-4 4.8e-3-5.1e-3 1.2e-3 4.2e-3 7.4e-3   0.01 1758.0    1.0\n",
      "L[22]    -9.2e-3  1.2e-4 4.8e-3  -0.02  -0.01-9.2e-3-6.0e-3 2.4e-4 1688.0    1.0\n",
      "L[23]     9.7e-3  1.1e-4 4.6e-3 5.1e-4 6.7e-3 9.7e-3   0.01   0.02 1731.0    1.0\n",
      "L[24]    -2.3e-3  1.1e-4 4.7e-3  -0.01-5.4e-3-2.3e-3 7.5e-4 6.8e-3 1760.0    1.0\n",
      "L[25]     1.9e-3  1.2e-4 4.8e-3-7.5e-3-1.2e-3 1.9e-3 5.0e-3   0.01 1650.0    1.0\n",
      "L[26]    -2.3e-3  1.5e-4 4.9e-3  -0.01-5.5e-3-2.4e-3 8.0e-4 7.4e-3 1076.0    1.0\n",
      "L[27]    -1.3e-3  1.4e-4 4.8e-3  -0.01-4.4e-3-1.4e-3 1.8e-3 8.3e-3 1252.0    1.0\n",
      "L[28]     5.1e-3  1.1e-4 4.9e-3-4.8e-3 1.9e-3 5.1e-3 8.3e-3   0.01 2098.0    1.0\n",
      "L[29]     3.2e-3  1.1e-4 5.0e-3-6.8e-3-6.9e-5 3.3e-3 6.5e-3   0.01 2074.0    1.0\n",
      "L[30]    -8.2e-3  1.2e-4 5.3e-3  -0.02  -0.01-8.2e-3-4.6e-3 1.9e-3 2019.0    1.0\n",
      "L[31]     3.6e-3  1.1e-4 5.0e-3-6.2e-3 2.8e-4 3.6e-3 7.0e-3   0.01 1969.0    1.0\n",
      "L[32]     3.9e-4  1.4e-4 4.9e-3  -0.01-2.6e-3 4.8e-4 3.6e-3 9.9e-3 1319.0    1.0\n",
      "L[33]    -4.2e-3  1.3e-4 4.9e-3  -0.01-7.4e-3-4.4e-3-1.1e-3 5.5e-3 1396.0    1.0\n",
      "L[34]     9.3e-3  1.3e-4 5.0e-3-8.0e-4 6.2e-3 9.4e-3   0.01   0.02 1560.0    1.0\n",
      "L[35]    -8.8e-3  1.3e-4 4.9e-3  -0.02  -0.01-8.9e-3-5.6e-3 1.1e-3 1515.0    1.0\n",
      "L[36]     5.1e-3  1.1e-4 4.5e-3-3.9e-3 2.1e-3 5.2e-3 8.1e-3   0.01 1561.0    1.0\n",
      "L[37]    -5.0e-3  1.1e-4 4.6e-3  -0.01-8.1e-3-5.0e-3-2.0e-3 4.2e-3 1933.0    1.0\n",
      "L[38]     4.4e-3  1.1e-4 4.7e-3-5.1e-3 1.3e-3 4.4e-3 7.6e-3   0.01 1882.0    1.0\n",
      "L[39]     1.3e-3  1.1e-4 4.8e-3-8.2e-3-2.0e-3 1.2e-3 4.5e-3   0.01 1898.0    1.0\n",
      "L[40]    -1.6e-3  1.1e-4 4.9e-3  -0.01-4.8e-3-1.6e-3 1.6e-3 8.0e-3 1913.0    1.0\n",
      "L[41]    -8.3e-4  1.1e-4 5.1e-3  -0.01-4.1e-3-7.8e-4 2.5e-3 9.1e-3 1978.0    1.0\n",
      "L[42]    -7.2e-4  1.1e-4 4.9e-3  -0.01-3.9e-3-7.2e-4 2.5e-3 9.1e-3 1984.0    1.0\n",
      "L[43]     7.9e-3  1.4e-4 5.1e-3-2.1e-3 4.5e-3 7.8e-3   0.01   0.02 1391.0    1.0\n",
      "L[44]    -9.2e-3  1.5e-4 5.3e-3  -0.02  -0.01-9.0e-3-5.6e-3 9.5e-4 1303.0    1.0\n",
      "L[45]    -1.2e-3  1.3e-4 5.4e-3  -0.01-4.7e-3-1.2e-3 2.2e-3   0.01 1705.0    1.0\n",
      "L[46]       0.01  1.2e-4 5.4e-3 7.7e-5 7.1e-3   0.01   0.01   0.02 1931.0    1.0\n",
      "L[47]    -4.4e-3  1.2e-4 5.6e-3  -0.02-8.0e-3-4.4e-3-8.6e-4 6.7e-3 2003.0    1.0\n",
      "L[48]     3.4e-3  1.4e-4 6.0e-3-8.4e-3-5.3e-4 3.4e-3 7.3e-3   0.02 1966.0    1.0\n",
      "L[49]    -1.5e-3  1.4e-4 6.0e-3  -0.01-5.6e-3-1.5e-3 2.5e-3   0.01 1785.0    1.0\n",
      "L[50]    -4.1e-3  1.4e-4 6.1e-3  -0.02-8.2e-3-4.1e-3-6.8e-5 7.9e-3 1975.0    1.0\n",
      "L[51]    -1.4e-3  1.3e-4 5.8e-3  -0.01-5.1e-3-1.4e-3 2.4e-3 9.8e-3 2034.0    1.0\n",
      "L[52]     3.2e-3  1.4e-4 6.1e-3-8.8e-3-7.9e-4 3.1e-3 7.2e-3   0.02 1805.0    1.0\n",
      "L[53]     6.0e-3  1.5e-4 6.1e-3-5.9e-3 1.9e-3 5.9e-310.0e-3   0.02 1750.0    1.0\n",
      "L[54]    -8.5e-3  1.6e-4 6.4e-3  -0.02  -0.01-8.4e-3-4.3e-3 3.8e-3 1637.0    1.0\n",
      "L[55]     6.4e-3  1.5e-4 6.0e-3-5.4e-3 2.4e-3 6.3e-3   0.01   0.02 1669.0    1.0\n",
      "L[56]    -4.5e-3  1.4e-4 5.7e-3  -0.02-8.2e-3-4.4e-3-6.6e-4 6.8e-3 1585.0    1.0\n",
      "L[57]    -7.6e-4  1.4e-4 5.8e-3  -0.01-4.6e-3-7.4e-4 3.0e-3   0.01 1654.0    1.0\n",
      "L[58]     5.7e-3  1.4e-4 5.8e-3-5.9e-3 1.9e-3 5.8e-3 9.6e-3   0.02 1678.0    1.0\n",
      "L[59]    -5.8e-3  1.6e-4 6.4e-3  -0.02  -0.01-5.8e-3-1.6e-3 6.6e-3 1671.0    1.0\n",
      "L[60]     6.1e-3  1.5e-4 6.0e-3-5.9e-3 2.1e-3 6.0e-3   0.01   0.02 1658.0    1.0\n",
      "L[61]    -5.0e-3  1.9e-4 6.4e-3  -0.02-9.3e-3-5.0e-3-7.4e-4 7.7e-3 1178.0    1.0\n",
      "L[62]     4.1e-3  1.8e-4 6.5e-3-8.4e-3-2.1e-4 4.2e-3 8.3e-3   0.02 1270.0    1.0\n",
      "L[63]    -2.3e-3  1.5e-4 6.2e-3  -0.01-6.4e-3-2.3e-3 1.8e-3   0.01 1849.0    1.0\n",
      "L[64]     3.1e-3  1.5e-4 6.5e-3  -0.01-1.1e-3 3.2e-3 7.3e-3   0.02 1880.0    1.0\n",
      "L[65]    -1.6e-3  1.5e-4 6.9e-3  -0.02-6.0e-3-1.7e-3 2.8e-3   0.01 2015.0    1.0\n",
      "L[66]    -4.8e-3  1.5e-4 6.4e-3  -0.02-8.9e-3-4.7e-3-5.9e-4 7.5e-3 1906.0    1.0\n",
      "L[67]     3.9e-3  1.5e-4 6.1e-3-8.1e-3 2.4e-6 4.0e-3 7.9e-3   0.02 1623.0    1.0\n",
      "L[68]    -1.2e-3  1.7e-4 6.1e-3  -0.01-5.1e-3-1.1e-3 2.8e-3   0.01 1360.0    1.0\n",
      "L[69]    -1.8e-3  1.6e-4 5.7e-3  -0.01-5.5e-3-1.9e-3 1.9e-3   0.01 1211.0    1.0\n",
      "L[70]     1.9e-3  1.5e-4 5.8e-3  -0.01-1.8e-3 1.9e-3 5.7e-3   0.01 1440.0    1.0\n",
      "L[71]    -6.2e-4  1.3e-4 5.3e-3  -0.01-4.1e-3-5.8e-4 3.0e-3 9.8e-3 1781.0    1.0\n",
      "L[72]    -3.2e-4  1.2e-4 5.1e-3  -0.01-3.6e-3-3.4e-4 3.0e-3 9.8e-3 1906.0    1.0\n",
      "L[73]     1.3e-3  1.2e-4 5.1e-3-8.8e-3-2.0e-3 1.3e-3 4.7e-3   0.01 1888.0    1.0\n",
      "L[74]    -3.6e-3  1.1e-4 5.0e-3  -0.01-6.8e-3-3.6e-3-4.3e-4 6.2e-3 1898.0    1.0\n",
      "L[75]     3.2e-3  1.1e-4 4.9e-3-6.5e-3-1.9e-5 3.2e-3 6.4e-3   0.01 1939.0    1.0\n",
      "L[76]    -1.2e-3  1.1e-4 4.8e-3  -0.01-4.4e-3-1.3e-3 2.0e-3 8.6e-3 1927.0    1.0\n",
      "L[77]     3.7e-3  1.2e-4 4.9e-3-6.1e-3 5.2e-4 3.7e-3 6.9e-3   0.01 1638.0    1.0\n",
      "L[78]    -7.1e-3  1.2e-4 4.8e-3  -0.02  -0.01-7.0e-3-3.9e-3 2.4e-3 1683.0    1.0\n",
      "L[79]     9.1e-3  1.2e-4 4.8e-3-1.8e-4 5.8e-3 9.1e-3   0.01   0.02 1598.0    1.0\n",
      "L[80]    -6.2e-3  1.1e-4 4.9e-3  -0.02-9.4e-3-6.1e-3-3.0e-3 3.4e-3 1796.0    1.0\n",
      "L[81]     1.1e-3  1.1e-4 4.8e-3-8.5e-3-2.0e-3 1.1e-3 4.3e-3   0.01 1853.0    1.0\n",
      "L[82]     3.3e-4  1.1e-4 4.8e-3-9.1e-3-2.8e-3 2.2e-4 3.5e-3 9.7e-3 2010.0    1.0\n",
      "L[83]     5.2e-3  1.0e-4 4.7e-3-3.9e-3 2.1e-3 5.2e-3 8.2e-3   0.01 2090.0    1.0\n",
      "L[84]    -2.5e-3  1.0e-4 4.7e-3  -0.01-5.5e-3-2.4e-3 5.7e-4 6.7e-3 2067.0    1.0\n",
      "L[85]    -3.8e-3  1.1e-4 4.8e-3  -0.01-7.0e-3-3.8e-3-6.6e-4 5.7e-3 2004.0    1.0\n",
      "L[86]     5.0e-3  1.1e-4 4.9e-3-4.7e-3 1.9e-3 5.0e-3 8.0e-3   0.01 1946.0    1.0\n",
      "L[87]     4.5e-3  1.2e-4 5.0e-3-5.4e-3 1.2e-3 4.5e-3 7.8e-3   0.01 1779.0    1.0\n",
      "L[88]    -8.0e-3  1.3e-4 5.3e-3  -0.02  -0.01-8.0e-3-4.5e-3 2.3e-3 1626.0    1.0\n",
      "L[89]    -2.3e-3  1.3e-4 5.5e-3  -0.01-6.0e-3-2.4e-3 1.3e-3 8.8e-3 1857.0    1.0\n",
      "L[90]     7.2e-3  1.5e-4 5.8e-3-4.3e-3 3.3e-3 7.2e-3   0.01   0.02 1577.0    1.0\n",
      "L[91]    -2.4e-3  1.8e-4 6.5e-3  -0.02-6.6e-3-2.4e-3 1.9e-3   0.01 1286.0    1.0\n",
      "L[92]    -7.8e-3  2.3e-4 7.2e-3  -0.02  -0.01-7.9e-3-2.9e-3 6.8e-3  997.0    1.0\n",
      "L[93]       0.02  2.3e-4 7.2e-3 7.0e-3   0.02   0.02   0.03   0.04  987.0    1.0\n",
      "L[94]      -0.03  2.5e-4 8.4e-3  -0.04  -0.03  -0.03  -0.02-9.0e-3 1134.0    1.0\n",
      "L[95]       0.01  2.4e-4 7.9e-3-6.2e-3 4.9e-3   0.01   0.02   0.03 1134.0    1.0\n",
      "L[96]    -1.2e-3  2.4e-4 8.4e-3  -0.02-7.1e-3-1.4e-3 4.1e-3   0.02 1196.0    1.0\n",
      "L[97]       0.01  2.2e-4 7.9e-3-5.6e-3 5.2e-3   0.01   0.02   0.03 1344.0    1.0\n",
      "L[98]      -0.01  2.1e-4 8.2e-3  -0.03  -0.02  -0.01-8.5e-3 2.2e-3 1573.0    1.0\n",
      "L[99]     8.9e-3  2.2e-4 8.6e-3-8.1e-3 3.1e-3 8.8e-3   0.01   0.03 1547.0    1.0\n",
      "L[100]   -7.2e-3  2.2e-4 8.7e-3  -0.02  -0.01-7.1e-3-1.4e-3   0.01 1595.0    1.0\n",
      "L[101]    5.7e-3  2.1e-4 8.6e-3  -0.01 8.0e-5 5.6e-3   0.01   0.02 1695.0    1.0\n",
      "L[102]   -1.8e-3  2.0e-4 8.2e-3  -0.02-7.2e-3-1.9e-3 3.4e-3   0.01 1730.0    1.0\n",
      "L[103]   -5.3e-3  2.0e-4 8.5e-3  -0.02  -0.01-5.2e-3 2.5e-4   0.01 1722.0    1.0\n",
      "L[104]      0.01  2.1e-4 8.5e-3-3.8e-3 7.2e-3   0.01   0.02   0.03 1637.0    1.0\n",
      "L[105]   -4.8e-3  2.3e-4 8.5e-3  -0.02  -0.01-4.8e-3 6.1e-4   0.01 1382.0    1.0\n",
      "L[106]   -5.3e-4  2.4e-4 9.1e-3  -0.02-6.3e-3-4.0e-4 5.4e-3   0.02 1427.0    1.0\n",
      "L[107]    1.1e-3  2.5e-4 9.4e-3  -0.02-5.1e-3 9.3e-4 7.1e-3   0.02 1380.0    1.0\n",
      "L[108]    7.4e-3  2.6e-4 9.7e-3  -0.01 1.2e-3 7.5e-3   0.01   0.03 1398.0    1.0\n",
      "L[109]   -9.0e-3  2.4e-4   0.01  -0.03  -0.02-8.9e-3-1.9e-3   0.01 1815.0    1.0\n",
      "L[110]    6.4e-3  2.6e-4   0.01  -0.01-9.3e-4 6.4e-3   0.01   0.03 1774.0    1.0\n",
      "L[111]   -2.5e-4  2.7e-4   0.01  -0.02-7.6e-3-2.2e-4 6.8e-3   0.02 1634.0    1.0\n",
      "L[112]   -5.4e-3  2.5e-4   0.01  -0.03  -0.01-5.4e-3 1.2e-3   0.01 1757.0    1.0\n",
      "L[113]    8.7e-3  2.5e-4   0.01  -0.01 1.9e-3 8.5e-3   0.02   0.03 1634.0    1.0\n",
      "L[114]   -4.2e-3  2.8e-4   0.01  -0.02  -0.01-4.0e-3 2.6e-3   0.02 1322.0    1.0\n",
      "L[115]    2.2e-3  2.3e-4   0.01  -0.02-4.5e-3 2.0e-3 8.8e-3   0.02 1913.0    1.0\n",
      "L[116]   -2.4e-3  2.1e-4 9.1e-3  -0.02-8.4e-3-2.3e-3 3.7e-3   0.02 1962.0    1.0\n",
      "L[117]    2.2e-3  2.2e-4 9.8e-3  -0.02-4.2e-3 2.1e-3 8.6e-3   0.02 1933.0    1.0\n",
      "L[118]   -2.2e-3  2.3e-4   0.01  -0.02-8.9e-3-2.1e-3 4.6e-3   0.02 1985.0    1.0\n",
      "L[119]    5.4e-3  2.3e-4   0.01  -0.01-1.5e-3 5.3e-3   0.01   0.03 1860.0    1.0\n",
      "L[120]   -7.7e-3  2.4e-4   0.01  -0.03  -0.01-7.6e-3-9.6e-4   0.01 1751.0    1.0\n",
      "L[121]    7.2e-3  2.4e-4 9.4e-3  -0.01 9.0e-4 7.1e-3   0.01   0.03 1578.0    1.0\n",
      "L[122]   -9.5e-3  2.1e-4 8.4e-3  -0.03  -0.02-9.6e-3-3.9e-3 7.2e-3 1657.0    1.0\n",
      "L[123]    7.1e-3  1.9e-4 7.5e-3-7.6e-3 2.0e-3 7.0e-3   0.01   0.02 1619.0    1.0\n",
      "L[124]   -5.6e-3  1.8e-4 7.8e-3  -0.02  -0.01-5.6e-3-5.2e-4 9.8e-3 1845.0    1.0\n",
      "L[125]    2.3e-3  1.6e-4 7.3e-3  -0.01-2.6e-3 2.3e-3 7.0e-3   0.02 1957.0    1.0\n",
      "L[126]    6.0e-3  1.6e-4 7.1e-3-7.9e-3 1.2e-3 5.9e-3   0.01   0.02 1868.0    1.0\n",
      "L[127]    4.0e-3  1.8e-4 7.5e-3  -0.01-9.0e-4 4.2e-3 9.0e-3   0.02 1836.0    1.0\n",
      "L[128]   -6.8e-3  1.7e-4 7.4e-3  -0.02  -0.01-6.9e-3-1.9e-3 7.7e-3 1888.0    1.0\n",
      "L[129]   -6.5e-3  1.5e-4 6.7e-3  -0.02  -0.01-6.6e-3-2.1e-3 6.5e-3 1961.0    1.0\n",
      "L[130]      0.01  1.8e-4 7.9e-3-4.0e-3 6.2e-3   0.01   0.02   0.03 1908.0    1.0\n",
      "L[131]   -5.5e-3  1.9e-4 8.5e-3  -0.02  -0.01-5.3e-3 2.0e-4   0.01 1952.0    1.0\n",
      "L[132]    3.6e-3  2.1e-4 9.2e-3  -0.01-2.7e-3 3.6e-3 9.7e-3   0.02 1929.0    1.0\n",
      "L[133]   -1.2e-3  2.2e-4 9.8e-3  -0.02-7.6e-3-1.0e-3 5.3e-3   0.02 1910.0    1.0\n",
      "L[134]    3.2e-3  2.1e-4 9.5e-3  -0.02-3.3e-3 3.2e-3 9.4e-3   0.02 1988.0    1.0\n",
      "L[135]   -3.1e-3  2.3e-4   0.01  -0.02-10.0e-3-3.0e-3 4.0e-3   0.02 2091.0    1.0\n",
      "L[136]    2.2e-3  2.4e-4   0.01  -0.02-4.7e-3 2.1e-3 9.1e-3   0.02 1847.0    1.0\n",
      "L[137]   -4.0e-4  2.4e-4   0.01  -0.02-7.4e-3-4.3e-4 6.7e-3   0.02 1916.0    1.0\n",
      "L[138]    1.5e-3  2.4e-4   0.01  -0.02-5.6e-3 1.4e-3 8.7e-3   0.02 2023.0    1.0\n",
      "L[139]   -2.0e-3  2.4e-4   0.01  -0.02-8.8e-3-1.9e-3 4.9e-3   0.02 1933.0    1.0\n",
      "L[140]    2.0e-3  2.5e-4   0.01  -0.02-5.2e-3 1.9e-3 9.1e-3   0.02 1864.0    1.0\n",
      "L[141]    4.6e-5  2.6e-4   0.01  -0.02-7.2e-3 1.5e-4 7.5e-3   0.02 1852.0    1.0\n",
      "L[142]   -1.7e-3  2.6e-4   0.01  -0.02-9.1e-3-1.7e-3 5.7e-3   0.02 1821.0    1.0\n",
      "L[143]    5.4e-3  3.0e-4   0.01  -0.02-2.6e-3 5.5e-3   0.01   0.03 1589.0    1.0\n",
      "L[144]   -6.6e-3  3.2e-4   0.01  -0.03  -0.01-6.6e-3 1.3e-3   0.02 1376.0    1.0\n",
      "L[145]    8.1e-3  2.8e-4   0.01  -0.01 1.2e-3 8.1e-3   0.02   0.03 1410.0    1.0\n",
      "L[146]   -7.7e-3  2.5e-4   0.01  -0.03  -0.01-7.4e-3-1.1e-3   0.01 1708.0    1.0\n",
      "L[147]    1.2e-3  2.5e-4   0.01  -0.02-6.0e-3 1.1e-3 7.9e-3   0.02 1741.0    1.0\n",
      "L[148]    3.8e-3  2.5e-4   0.01  -0.02-3.6e-3 4.0e-3   0.01   0.02 1866.0    1.0\n",
      "L[149]    5.6e-4  2.8e-4   0.01  -0.02-6.8e-3 7.7e-4 8.0e-3   0.02 1645.0    1.0\n",
      "L[150]   -9.9e-4  2.6e-4   0.01  -0.02-8.4e-3-1.1e-3 6.3e-3   0.02 1776.0    1.0\n",
      "L[151]    5.9e-3  2.9e-4   0.01  -0.02-1.7e-3 6.0e-3   0.01   0.03 1502.0    1.0\n",
      "L[152]   -9.7e-3  2.5e-4 9.8e-3  -0.03  -0.02-9.6e-3-2.9e-3 8.5e-3 1600.0    1.0\n",
      "L[153]      0.01  1.9e-4 8.1e-3-2.4e-3 8.4e-3   0.01   0.02   0.03 1874.0    1.0\n",
      "L[154]    5.2e-4  1.6e-4 6.9e-3  -0.01-4.0e-3 5.0e-4 5.1e-3   0.01 1807.0    1.0\n",
      "L[155]    1.3e-3  1.8e-4 7.9e-3  -0.01-3.8e-3 1.3e-3 6.6e-3   0.02 1879.0    1.0\n",
      "L[156]    6.9e-4  1.9e-4 8.6e-3  -0.02-5.0e-3 8.0e-4 6.4e-3   0.02 2089.0    1.0\n",
      "L[157]   -6.5e-3  1.9e-4 8.3e-3  -0.02  -0.01-6.5e-3-9.8e-4 9.8e-3 1945.0    1.0\n",
      "L[158]    2.4e-3  1.9e-4 8.4e-3  -0.01-3.2e-3 2.5e-3 8.1e-3   0.02 1965.0    1.0\n",
      "L[159]    4.5e-3  1.9e-4 8.5e-3  -0.01-1.1e-3 4.4e-3 9.9e-3   0.02 2000.0    1.0\n",
      "L[160]   -6.1e-3  2.1e-4 9.6e-3  -0.03  -0.01-6.1e-3 3.1e-4   0.01 2055.0    1.0\n",
      "L[161]    4.6e-3  2.2e-4 9.9e-3  -0.01-2.1e-3 4.6e-3   0.01   0.02 1956.0    1.0\n",
      "L[162]   -1.0e-3  2.6e-4   0.01  -0.02-8.3e-3-9.3e-4 6.3e-3   0.02 1724.0    1.0\n",
      "L[163]    7.8e-4  2.6e-4   0.01  -0.02-6.6e-3 6.3e-4 8.1e-3   0.02 1739.0    1.0\n",
      "L[164]   -3.3e-3  2.5e-4   0.01  -0.03  -0.01-3.2e-3 3.7e-3   0.02 1929.0    1.0\n",
      "L[165]    2.9e-3  2.7e-4   0.01  -0.02-4.4e-3 2.9e-3   0.01   0.02 1617.0    1.0\n",
      "L[166]   -6.9e-4  2.6e-4   0.01  -0.02-8.2e-3-5.1e-4 6.7e-3   0.02 1884.0    1.0\n",
      "L[167]    9.0e-4  3.3e-4   0.01  -0.02-6.9e-3 6.7e-4 8.5e-3   0.03 1263.0    1.0\n",
      "L[168]   -2.4e-3  2.6e-4   0.01  -0.03-9.4e-3-2.1e-3 5.0e-3   0.02 1853.0    1.0\n",
      "L[169]    5.4e-3  2.7e-4   0.01  -0.02-1.9e-3 5.2e-3   0.01   0.03 1704.0    1.0\n",
      "L[170]   -4.5e-3  3.0e-4   0.01  -0.03  -0.01-4.5e-3 3.0e-3   0.02 1459.0    1.0\n",
      "L[171]    1.1e-3  2.5e-4   0.01  -0.02-5.8e-3 9.6e-4 8.1e-3   0.02 1750.0    1.0\n",
      "L[172]   -3.2e-3  2.3e-4 9.6e-3  -0.02-9.5e-3-3.3e-3 3.1e-3   0.02 1686.0    1.0\n",
      "L[173]    5.7e-3  2.9e-4 9.7e-3  -0.01-5.7e-4 5.8e-3   0.01   0.02 1091.0    1.0\n",
      "L[174]    1.1e-3  2.3e-4 9.4e-3  -0.02-4.9e-3 9.8e-4 7.2e-3   0.02 1715.0    1.0\n",
      "L[175]   -2.3e-4  2.1e-4 8.8e-3  -0.02-6.1e-3-1.0e-4 5.4e-3   0.02 1798.0    1.0\n",
      "L[176]   -3.7e-3  2.3e-4 9.2e-3  -0.02-9.8e-3-3.6e-3 2.2e-3   0.01 1562.0    1.0\n",
      "L[177]    4.0e-3  2.1e-4   0.01  -0.02-2.7e-3 3.7e-3   0.01   0.02 2285.0    1.0\n",
      "un          4.23  6.2e-3    0.2   3.83    4.1   4.23   4.36   4.62 1060.0    1.0\n",
      "beta[0]      1.4    0.17   2.76  -3.33   0.73    1.1   1.84   8.04  268.0   1.01\n",
      "beta[1]     0.72    0.06   0.95  -1.73   0.46   0.92   1.08   2.34  270.0   1.01\n",
      "beta[2]     1.16    0.02   0.41   0.48   0.96   1.06    1.3   2.22  332.0   1.02\n",
      "beta[3]     0.93    0.01   0.28   0.19   0.87   0.98   1.04   1.43  449.0   1.01\n",
      "beta[4]     1.01    0.01    0.2   0.61   0.95    1.0   1.06   1.47  353.0    1.0\n",
      "beta[5]     1.01  6.8e-3   0.12   0.78   0.97   1.01   1.04   1.28  305.0    1.0\n",
      "beta[6]     0.99  3.5e-3   0.06   0.84   0.97   0.99   1.01    1.1  317.0    1.0\n",
      "beta[7]     1.01  1.5e-3   0.03   0.95   0.99   1.01   1.02   1.09  540.0    1.0\n",
      "beta[8]     0.99  8.0e-4   0.02   0.95   0.98   0.99    1.0   1.04  813.0    1.0\n",
      "beta[9]      1.0  6.8e-4   0.02   0.97   0.99    1.0   1.01   1.04  703.0   1.01\n",
      "beta[10]    0.99  6.2e-4   0.02   0.96   0.98   0.99    1.0   1.02  622.0    1.0\n",
      "beta[11]    1.01  5.6e-4   0.01   0.99    1.0   1.01   1.02   1.05  698.0    1.0\n",
      "beta[12]    0.98  4.9e-4   0.01   0.95   0.97   0.98   0.99    1.0  828.0    1.0\n",
      "beta[13]    1.01  4.2e-4   0.01   0.99    1.0   1.01   1.02   1.04  902.0    1.0\n",
      "beta[14]     1.0  2.8e-4   0.01   0.98   0.99    1.0   1.01   1.02 1474.0    1.0\n",
      "beta[15]     1.0  2.5e-4   0.01   0.98   0.99    1.0   1.01   1.02 1666.0    1.0\n",
      "beta[16]     1.0  3.5e-4   0.01   0.98    1.0    1.0   1.01   1.03 1003.0    1.0\n",
      "beta[17]    0.99  4.0e-4   0.01   0.96   0.98   0.99    1.0   1.01  891.0    1.0\n",
      "beta[18]    1.01  4.0e-4   0.01   0.99   1.01   1.01   1.02   1.04  912.0    1.0\n",
      "beta[19]    0.99  3.1e-4   0.01   0.97   0.99   0.99    1.0   1.01 1216.0    1.0\n",
      "beta[20]     1.0  2.5e-4   0.01   0.98    1.0    1.0   1.01   1.02 1628.0    1.0\n",
      "beta[21]     1.0  2.3e-4 9.8e-3   0.98   0.99    1.0   1.01   1.02 1843.0    1.0\n",
      "beta[22]     1.0  2.4e-410.0e-3   0.98   0.99    1.0    1.0   1.02 1736.0    1.0\n",
      "beta[23]     1.0  2.5e-4   0.01   0.98    1.0    1.0   1.01   1.03 1718.0    1.0\n",
      "beta[24]     1.0  2.4e-4   0.01   0.98   0.99    1.0    1.0   1.02 1747.0    1.0\n",
      "beta[25]     1.0  2.5e-4   0.01   0.98   0.99    1.0   1.01   1.02 1671.0    1.0\n",
      "beta[26]     1.0  3.0e-4   0.01   0.98   0.99    1.0   1.01   1.02 1147.0    1.0\n",
      "beta[27]     1.0  2.7e-4   0.01   0.98   0.99    1.0   1.01   1.02 1375.0    1.0\n",
      "beta[28]     1.0  2.1e-4 9.7e-3   0.98   0.99    1.0   1.01   1.02 2127.0    1.0\n",
      "beta[29]     1.0  2.1e-4 9.5e-3   0.98    1.0    1.0   1.01   1.02 2098.0    1.0\n",
      "beta[30]     1.0  2.1e-4 9.6e-3   0.98   0.99    1.0    1.0   1.02 2045.0    1.0\n",
      "beta[31]     1.0  2.2e-4 9.8e-3   0.98   0.99    1.0   1.01   1.02 1999.0    1.0\n",
      "beta[32]     1.0  2.8e-4   0.01   0.98   0.99    1.0   1.01   1.02 1321.0    1.0\n",
      "beta[33]     1.0  2.7e-4   0.01   0.98   0.99    1.0    1.0   1.02 1463.0    1.0\n",
      "beta[34]    1.01  2.7e-4   0.01   0.99    1.0   1.01   1.01   1.03 1475.0    1.0\n",
      "beta[35]     1.0  2.6e-4   0.01   0.97   0.99    1.0    1.0   1.01 1492.0    1.0\n",
      "beta[36]     1.0  2.6e-4   0.01   0.98    1.0    1.0   1.01   1.02 1568.0    1.0\n",
      "beta[37]     1.0  2.3e-410.0e-3   0.98   0.99    1.0    1.0   1.02 1960.0    1.0\n",
      "beta[38]     1.0  2.3e-4 9.9e-3   0.98    1.0    1.0   1.01   1.02 1876.0    1.0\n",
      "beta[39]     1.0  2.2e-4 9.6e-3   0.98   0.99    1.0   1.01   1.02 1934.0    1.0\n",
      "beta[40]     1.0  2.1e-4 9.5e-3   0.98   0.99    1.0   1.01   1.02 1993.0    1.0\n",
      "beta[41]     1.0  2.1e-4 9.5e-3   0.98   0.99    1.0   1.01   1.02 2035.0    1.0\n",
      "beta[42]     1.0  2.2e-4 9.6e-3   0.98   0.99    1.0   1.01   1.02 2003.0    1.0\n",
      "beta[43]     1.0  2.3e-4 9.9e-3   0.98    1.0    1.0   1.01   1.02 1804.0    1.0\n",
      "beta[44]     1.0  2.6e-4 9.9e-3   0.98   0.99    1.0    1.0   1.02 1413.0    1.0\n",
      "beta[45]     1.0  2.3e-4 9.7e-3   0.98   0.99    1.0   1.01   1.02 1827.0    1.0\n",
      "beta[46]    1.01  2.2e-4 9.7e-3   0.99    1.0   1.01   1.01   1.02 1901.0    1.0\n",
      "beta[47]     1.0  2.1e-4 9.5e-3   0.98   0.99    1.0    1.0   1.02 2004.0    1.0\n",
      "beta[48]     1.0  2.1e-4 9.2e-3   0.98    1.0    1.0   1.01   1.02 1997.0    1.0\n",
      "beta[49]     1.0  2.1e-4 9.0e-3   0.98   0.99    1.0    1.0   1.02 1846.0    1.0\n",
      "beta[50]     1.0  2.0e-4 8.9e-3   0.98   0.99    1.0    1.0   1.02 1985.0    1.0\n",
      "beta[51]     1.0  2.0e-4 9.0e-3   0.98   0.99    1.0   1.01   1.02 2064.0    1.0\n",
      "beta[52]     1.0  2.1e-4 8.9e-3   0.98   0.99    1.0   1.01   1.02 1878.0    1.0\n",
      "beta[53]     1.0  2.1e-4 8.9e-3   0.99    1.0    1.0   1.01   1.02 1740.0    1.0\n",
      "beta[54]    0.99  2.2e-4 9.1e-3   0.98   0.99   0.99    1.0   1.01 1696.0    1.0\n",
      "beta[55]     1.0  2.1e-4 9.1e-3   0.99    1.0    1.0   1.01   1.02 1802.0    1.0\n",
      "beta[56]     1.0  2.3e-4 9.1e-3   0.98   0.99    1.0    1.0   1.02 1627.0    1.0\n",
      "beta[57]     1.0  2.2e-4 9.2e-3   0.98   0.99    1.0   1.01   1.02 1699.0    1.0\n",
      "beta[58]     1.0  2.2e-4 9.2e-3   0.99    1.0    1.0   1.01   1.02 1698.0    1.0\n",
      "beta[59]     1.0  2.1e-4 8.9e-3   0.98   0.99    1.0    1.0   1.01 1742.0    1.0\n",
      "beta[60]     1.0  2.2e-4 9.1e-3   0.99    1.0    1.0   1.01   1.02 1667.0    1.0\n",
      "beta[61]     1.0  2.6e-4 9.0e-3   0.98   0.99    1.0    1.0   1.01 1224.0    1.0\n",
      "beta[62]     1.0  2.4e-4 8.8e-3   0.99    1.0    1.0   1.01   1.02 1378.0    1.0\n",
      "beta[63]     1.0  2.0e-4 8.5e-3   0.98   0.99    1.0    1.0   1.01 1809.0    1.0\n",
      "beta[64]     1.0  1.9e-4 8.3e-3   0.99    1.0    1.0   1.01   1.02 1865.0    1.0\n",
      "beta[65]     1.0  2.0e-4 8.2e-3   0.98   0.99    1.0    1.0   1.01 1754.0    1.0\n",
      "beta[66]     1.0  2.0e-4 8.8e-3   0.98   0.99    1.0    1.0   1.02 1950.0    1.0\n",
      "beta[67]     1.0  2.0e-4 9.0e-3   0.98    1.0    1.0   1.01   1.02 1957.0    1.0\n",
      "beta[68]     1.0  2.4e-4 9.1e-3   0.98   0.99    1.0    1.0   1.02 1476.0    1.0\n",
      "beta[69]     1.0  2.7e-4 9.6e-3   0.98   0.99    1.0   1.01   1.02 1291.0    1.0\n",
      "beta[70]     1.0  2.4e-4 9.4e-3   0.98   0.99    1.0   1.01   1.02 1546.0    1.0\n",
      "beta[71]     1.0  2.2e-4 9.3e-3   0.98   0.99    1.0   1.01   1.02 1814.0    1.0\n",
      "beta[72]     1.0  2.1e-4 9.4e-3   0.98   0.99    1.0   1.01   1.02 1903.0    1.0\n",
      "beta[73]     1.0  2.2e-4 9.5e-3   0.98   0.99    1.0   1.01   1.02 1892.0    1.0\n",
      "beta[74]     1.0  2.2e-4 9.7e-3   0.98   0.99    1.0    1.0   1.02 1926.0    1.0\n",
      "beta[75]     1.0  2.2e-4 9.7e-3   0.98    1.0    1.0   1.01   1.02 1943.0    1.0\n",
      "beta[76]     1.0  2.3e-4 9.7e-3   0.98   0.99    1.0    1.0   1.02 1852.0    1.0\n",
      "beta[77]     1.0  2.5e-4 9.9e-3   0.98    1.0    1.0   1.01   1.02 1610.0    1.0\n",
      "beta[78]     1.0  2.4e-410.0e-3   0.98   0.99    1.0    1.0   1.02 1685.0    1.0\n",
      "beta[79]     1.0  2.5e-4   0.01   0.99    1.0    1.0   1.01   1.02 1611.0    1.0\n",
      "beta[80]     1.0  2.4e-4 9.9e-3   0.98   0.99    1.0    1.0   1.02 1767.0    1.0\n",
      "beta[81]     1.0  2.3e-4 9.8e-3   0.98    1.0    1.0   1.01   1.02 1905.0    1.0\n",
      "beta[82]     1.0  2.2e-4 9.8e-3   0.98   0.99    1.0   1.01   1.02 2032.0    1.0\n",
      "beta[83]     1.0  2.1e-4 9.8e-3   0.98    1.0    1.0   1.01   1.02 2130.0    1.0\n",
      "beta[84]     1.0  2.1e-4 9.8e-3   0.98   0.99    1.0   1.01   1.02 2092.0    1.0\n",
      "beta[85]     1.0  2.2e-4 9.8e-3   0.98   0.99    1.0   1.01   1.02 2033.0    1.0\n",
      "beta[86]     1.0  2.2e-4 9.7e-3   0.98   0.99    1.0   1.01   1.02 1961.0    1.0\n",
      "beta[87]     1.0  2.2e-4 9.6e-3   0.98    1.0    1.0   1.01   1.02 1850.0    1.0\n",
      "beta[88]     1.0  2.3e-4 9.7e-3   0.98   0.99    1.0    1.0   1.02 1834.0    1.0\n",
      "beta[89]     1.0  2.2e-4 9.5e-3   0.98   0.99    1.0   1.01   1.02 1883.0    1.0\n",
      "beta[90]     1.0  2.4e-4 9.6e-3   0.98    1.0    1.0   1.01   1.02 1568.0    1.0\n",
      "beta[91]     1.0  2.5e-4 9.3e-3   0.98    1.0    1.0   1.01   1.02 1410.0    1.0\n",
      "beta[92]    0.99  2.7e-4 9.3e-3   0.97   0.99   0.99    1.0   1.01 1172.0    1.0\n",
      "beta[93]    1.02  3.3e-4   0.01    1.0   1.01   1.02   1.02   1.04 1049.0    1.0\n",
      "beta[94]    0.98  3.2e-4   0.01   0.96   0.97   0.98   0.99    1.0 1060.0    1.0\n",
      "beta[95]    1.01  3.0e-4   0.01   0.99    1.0   1.01   1.02   1.03 1138.0    1.0\n",
      "beta[96]    0.99  2.7e-4 9.6e-3   0.97   0.99   0.99    1.0   1.01 1265.0    1.0\n",
      "beta[97]    1.01  2.6e-4 9.3e-3   0.99   1.01   1.01   1.02   1.03 1308.0    1.0\n",
      "beta[98]    0.99  2.3e-4 8.6e-3   0.97   0.98   0.99   0.99    1.0 1453.0    1.0\n",
      "beta[99]    1.01  2.0e-4 7.7e-3   0.99    1.0   1.01   1.01   1.02 1527.0    1.0\n",
      "beta[100]   0.99  1.8e-4 7.3e-3   0.98   0.99   0.99    1.0   1.01 1618.0    1.0\n",
      "beta[101]   1.01  1.8e-4 7.0e-3   0.99    1.0   1.01   1.01   1.02 1592.0    1.0\n",
      "beta[102]    1.0  1.6e-4 7.0e-3   0.98   0.99    1.0    1.0   1.01 1887.0    1.0\n",
      "beta[103]    1.0  1.6e-4 7.0e-3   0.98   0.99    1.0    1.0   1.01 1847.0    1.0\n",
      "beta[104]   1.01  2.0e-4 7.8e-3    1.0   1.01   1.01   1.02   1.03 1480.0    1.0\n",
      "beta[105]   0.99  1.9e-4 7.4e-3   0.98   0.99   0.99    1.0   1.01 1499.0    1.0\n",
      "beta[106]    1.0  1.8e-4 7.0e-3   0.99    1.0    1.0   1.01   1.02 1528.0    1.0\n",
      "beta[107]   0.99  1.7e-4 6.6e-3   0.98   0.99   0.99    1.0   1.01 1562.0    1.0\n",
      "beta[108]   1.01  1.6e-4 6.0e-3    1.0   1.01   1.01   1.02   1.02 1355.0    1.0\n",
      "beta[109]   0.99  8.8e-5 3.5e-3   0.99   0.99   0.99    1.0    1.0 1577.0    1.0\n",
      "beta[110]   0.99  8.3e-5 3.1e-3   0.99   0.99   0.99   0.99    1.0 1426.0   1.01\n",
      "beta[111]   1.01  8.1e-5 3.3e-3    1.0   1.01   1.01   1.01   1.02 1714.0    1.0\n",
      "beta[112]    1.0  1.0e-4 4.5e-3   0.99    1.0    1.0    1.0   1.01 1933.0    1.0\n",
      "beta[113]   0.99  1.4e-4 5.7e-3   0.98   0.99   0.99    1.0    1.0 1575.0    1.0\n",
      "beta[114]   1.01  1.5e-4 5.3e-3   0.99    1.0   1.01   1.01   1.02 1209.0    1.0\n",
      "beta[115]    1.0  1.1e-4 4.7e-3   0.99   0.99    1.0    1.0   1.01 1918.0    1.0\n",
      "beta[116]    1.0  1.3e-4 5.7e-3   0.99    1.0    1.0   1.01   1.02 2010.0    1.0\n",
      "beta[117]    1.0  1.1e-4 5.0e-3   0.99   0.99    1.0    1.0   1.01 1978.0    1.0\n",
      "beta[118]   1.01  1.1e-4 5.0e-3    1.0    1.0   1.01   1.01   1.02 2145.0    1.0\n",
      "beta[119]   0.99  1.3e-4 5.5e-3   0.98   0.99   0.99   0.99    1.0 1791.0    1.0\n",
      "beta[120]   1.01  1.5e-4 6.0e-3    1.0   1.01   1.02   1.02   1.03 1544.0    1.0\n",
      "beta[121]   0.99  1.7e-4 6.6e-3   0.98   0.98   0.99   0.99    1.0 1490.0    1.0\n",
      "beta[122]   1.01  2.1e-4 8.0e-3    1.0   1.01   1.01   1.02   1.03 1455.0    1.0\n",
      "beta[123]   0.99  2.2e-4 8.5e-3   0.98   0.99   0.99    1.0   1.01 1499.0    1.0\n",
      "beta[124]   1.01  1.9e-4 8.3e-3   0.99    1.0   1.01   1.01   1.02 1805.0    1.0\n",
      "beta[125]    1.0  1.9e-4 8.5e-3   0.98   0.99    1.0    1.0   1.01 1960.0    1.0\n",
      "beta[126]    1.0  2.0e-4 8.5e-3   0.98   0.99    1.0    1.0   1.02 1795.0    1.0\n",
      "beta[127]    1.0  1.9e-4 8.1e-3   0.98   0.99    1.0    1.0   1.01 1812.0    1.0\n",
      "beta[128]   1.01  1.9e-4 8.2e-3   0.99    1.0   1.01   1.01   1.02 1805.0    1.0\n",
      "beta[129]    1.0  1.8e-4 8.1e-3   0.99    1.0    1.0   1.01   1.02 1961.0    1.0\n",
      "beta[130]   0.99  1.8e-4 7.6e-3   0.98   0.99   0.99    1.0   1.01 1900.0    1.0\n",
      "beta[131]    1.0  1.6e-4 7.0e-3   0.99    1.0    1.0   1.01   1.02 1988.0    1.0\n",
      "beta[132]    1.0  1.4e-4 6.3e-3   0.98   0.99    1.0    1.0   1.01 2025.0    1.0\n",
      "beta[133]    1.0  1.2e-4 5.4e-3   0.99    1.0    1.0   1.01   1.01 1937.0    1.0\n",
      "beta[134]   0.99  1.2e-4 5.4e-3   0.98   0.99   0.99    1.0    1.0 2030.0    1.0\n",
      "beta[135]   1.01  9.0e-5 4.2e-3    1.0   1.01   1.01   1.01   1.02 2121.0    1.0\n",
      "beta[136]   0.99  1.0e-4 4.0e-3   0.99   0.99   0.99    1.0    1.0 1571.0    1.0\n",
      "beta[137]    1.0  7.3e-5 3.3e-3    1.0    1.0    1.0   1.01   1.01 2036.0    1.0\n",
      "beta[138]   0.99  5.6e-5 2.8e-3   0.99   0.99   0.99   0.99    1.0 2468.0    1.0\n",
      "beta[139]   1.01  7.7e-5 3.3e-3    1.0   1.01   1.01   1.01   1.02 1850.0    1.0\n",
      "beta[140]   0.99  5.2e-5 2.6e-3   0.99   0.99   0.99   0.99    1.0 2552.0    1.0\n",
      "beta[141]   1.01  5.5e-5 2.7e-3    1.0   1.01   1.01   1.01   1.02 2498.0    1.0\n",
      "beta[142]   0.99  6.5e-5 3.0e-3   0.98   0.98   0.99   0.99   0.99 2154.0    1.0\n",
      "beta[143]   1.02  9.9e-5 3.9e-3   1.01   1.02   1.02   1.02   1.03 1581.0    1.0\n",
      "beta[144]   0.98  1.1e-4 4.2e-3   0.97   0.98   0.98   0.98   0.99 1514.0    1.0\n",
      "beta[145]   1.02  1.5e-4 5.6e-3   1.01   1.02   1.02   1.02   1.03 1421.0    1.0\n",
      "beta[146]   0.99  1.4e-4 5.5e-3   0.98   0.98   0.99   0.99    1.0 1567.0    1.0\n",
      "beta[147]    1.0  1.0e-4 4.5e-3   0.99    1.0    1.0    1.0   1.01 1993.0    1.0\n",
      "beta[148]    1.0  1.0e-4 4.4e-3   0.99    1.0    1.0   1.01   1.01 1833.0    1.0\n",
      "beta[149]   1.01  9.4e-5 3.9e-3    1.0   1.01   1.01   1.02   1.02 1769.0    1.0\n",
      "beta[150]   0.98  8.3e-5 3.6e-3   0.98   0.98   0.98   0.99   0.99 1843.0   1.01\n",
      "beta[151]   1.02  9.4e-5 3.7e-3   1.01   1.02   1.02   1.03   1.03 1560.0    1.0\n",
      "beta[152]   0.98  9.9e-5 4.1e-3   0.98   0.98   0.98   0.99   0.99 1707.0    1.0\n",
      "beta[153]    1.0  1.8e-4 7.4e-3   0.99   0.99    1.0    1.0   1.01 1672.0    1.0\n",
      "beta[154]    1.0  1.9e-4 8.2e-3   0.99    1.0    1.0   1.01   1.02 1926.0    1.0\n",
      "beta[155]   0.99  1.8e-4 7.6e-3   0.98   0.99   0.99    1.0   1.01 1827.0    1.0\n",
      "beta[156]   1.01  1.6e-4 6.9e-3   0.99    1.0   1.01   1.01   1.02 1880.0    1.0\n",
      "beta[157]    1.0  1.7e-4 7.5e-3   0.99    1.0    1.0   1.01   1.02 1907.0    1.0\n",
      "beta[158]    1.0  1.6e-4 7.1e-3   0.99    1.0    1.0   1.01   1.02 1968.0    1.0\n",
      "beta[159]   0.99  1.5e-4 6.6e-3   0.98   0.99   0.99    1.0   1.01 2076.0    1.0\n",
      "beta[160]   1.01  1.2e-4 5.5e-3    1.0   1.01   1.01   1.01   1.02 2086.0    1.0\n",
      "beta[161]    1.0  1.0e-4 4.8e-3   0.99   0.99   0.99    1.0   1.01 2134.0    1.0\n",
      "beta[162]    1.0  1.0e-4 3.7e-3   0.99    1.0    1.0    1.0   1.01 1283.0    1.0\n",
      "beta[163]    1.0  9.4e-5 3.7e-3   0.99    1.0    1.0    1.0   1.01 1557.0    1.0\n",
      "beta[164]   1.01  9.0e-5 3.9e-3    1.0   1.01   1.01   1.01   1.01 1911.0    1.0\n",
      "beta[165]   0.99  7.1e-5 3.3e-3   0.99   0.99   0.99    1.0    1.0 2116.0    1.0\n",
      "beta[166]    1.0  6.6e-5 2.8e-3    1.0    1.0    1.0    1.0   1.01 1751.0    1.0\n",
      "beta[167]   0.99  8.4e-5 2.9e-3   0.98   0.99   0.99   0.99    1.0 1195.0   1.01\n",
      "beta[168]   1.01  6.2e-5 2.7e-3    1.0    1.0   1.01   1.01   1.01 1937.0    1.0\n",
      "beta[169]   1.01  8.5e-5 3.5e-3    1.0   1.01   1.01   1.01   1.02 1744.0    1.0\n",
      "beta[170]   0.98  1.1e-4 4.1e-3   0.98   0.98   0.98   0.98   0.99 1445.0    1.0\n",
      "beta[171]   1.01  1.1e-4 4.4e-3    1.0    1.0   1.01   1.01   1.02 1471.0    1.0\n",
      "beta[172]   0.99  1.6e-4 5.7e-3   0.98   0.99   0.99    1.0    1.0 1343.0    1.0\n",
      "beta[173]   1.01  1.6e-4 5.9e-3    1.0   1.01   1.01   1.01   1.02 1347.0    1.0\n",
      "beta[174]    1.0  1.4e-4 5.9e-3   0.99    1.0    1.0    1.0   1.01 1844.0    1.0\n",
      "beta[175]    1.0  1.5e-4 6.6e-3   0.99    1.0    1.0   1.01   1.01 1946.0    1.0\n",
      "beta[176]   0.99  1.6e-4 6.6e-3   0.98   0.99   0.99    1.0   1.01 1798.0    1.0\n",
      "beta[177]   1.01  1.8e-4 8.8e-3   0.99    1.0   1.01   1.01   1.02 2477.0    1.0\n",
      "uc[0]       0.05  5.0e-4   0.03-3.2e-3   0.03   0.05   0.06    0.1 2733.0    1.0\n",
      "uc[1]       1.32  1.7e-3   0.07   1.18   1.27   1.32   1.37   1.46 1812.0    1.0\n",
      "uc[2]      -0.33  1.7e-3   0.07  -0.47  -0.38  -0.33  -0.28  -0.19 1853.0    1.0\n",
      "cL[0]     5.2e-4  1.7e-5 8.8e-4-1.2e-3-7.2e-5 5.1e-4 1.1e-3 2.2e-3 2682.0    nan\n",
      "cL[1]      -0.89  4.6e-3   0.11  -1.08  -0.96   -0.9  -0.83  -0.65  595.0    1.0\n",
      "cL[2]      -0.33  3.6e-3    0.1  -0.54  -0.39  -0.33  -0.26  -0.15  784.0    1.0\n",
      "b[0]        2.28  7.6e-3   0.21    1.9   2.15   2.27    2.4   2.74  744.0    1.0\n",
      "b[1]       -0.97  4.5e-3   0.12  -1.24  -1.04  -0.96  -0.89  -0.75  755.0    1.0\n",
      "b[2]       -0.31  3.5e-3    0.1  -0.52  -0.37  -0.31  -0.25  -0.12  806.0    1.0\n",
      "sb        6.9e-3  2.6e-5 9.3e-4 5.1e-3 6.3e-3 6.9e-3 7.5e-3 8.8e-3 1232.0    nan\n",
      "su          0.12  1.3e-4 6.6e-3   0.11   0.12   0.12   0.13   0.14 2679.0    1.0\n",
      "sL        7.7e-3  2.6e-5 8.3e-4 6.1e-3 7.1e-3 7.7e-3 8.2e-3 9.3e-3 1017.0    nan\n",
      "s         1.7e-3  3.4e-4 8.3e-4 3.0e-4 1.2e-3 1.6e-3 2.2e-3 3.6e-3    6.0    nan\n",
      "lp__      3297.4   43.98  98.35 3155.3 3231.3 3279.2 3339.3 3568.2    5.0   1.22\n",
      "\n",
      "Samples were drawn using NUTS(diag_e) at Sun Sep 25 05:20:52 2016.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "fit_fullstate,res_fullstate=runmodel(model_fullstate,data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"model_fullstate\",\"wb\") as fpp:\n",
    "    pkl.dump(res_fullstate,fpp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f23452fe410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2344532290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IB2rWcR/BFE27j8Csf3Ty6aNfBdbmk0DqRB7xUeAnafoO4DRJe0g6DDgCeLAdwZqZWfs1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\nck4EZmYl50RgZlZy/x/E4Isq++DrcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234419a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(res_fullstate['un'],bins=100)\n",
    "plt.title(\"natural unemployment rate\")\n",
    "plt.show()\n",
    "plt.hist(res_fullstate['su'],bins=100)\n",
    "plt.title(\"sigma of unemployment rate\")\n",
    "plt.show()\n",
    "plt.hist(res_fullstate['s'],bins=100)\n",
    "plt.title(\"sigma of price prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NH2CDBg+ySpUrWLJkTUZFlWGFCo1YsWIzFitWgqVK1WKbNqNYrlzWA7xu3Z7s2PEbxsRU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\nPO7/A5YU0MNrLI1lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2343f387d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawline(res_fullstate,\"beta\",5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SAwNSOY91Fko47e3HZuaDNIDRlWvDhs10dU3svvigsnHj0btcJkO4u8l63e3QkIxWOzpg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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2345406950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawline(res_fullstate,\"L\",2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}