tmarthal/Linear Dependent VAR

## Linear Dependent VAR
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import statsmodels.api as sm\n",
      "from statsmodels.tsa.base.datetools import dates_from_str\n",
      "from statsmodels.tsa.api import VAR"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels\n",
      "print statsmodels.__version__\n",
      "print pd.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.5.0\n",
        "0.12.0\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdata = sm.datasets.macrodata.load_pandas().data\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#mdata[['year', 'realgdp']].plot()\n",
      "plt.plot(mdata['year'], mdata['realgdp'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<matplotlib.lines.Line2D at 0x113ac2ad0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kpqaajRs3mqamJnPdddeZVatWtfgMP4Qpp9HYaMz48cZcdpkxERHG9O5tzJVX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\nRER8UrEQERGfVCxERMSn/w8sYoUC++sGIQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113a88210>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# prepare the dates index\n",
      "dates = mdata[['year', 'quarter']].astype(int).astype(str)\n",
      "quarterly = dates[\"year\"] + \"Q\" + dates[\"quarter\"]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This changes 2008Q4 to relevant datetime\n",
      "quarterly = dates_from_str(quarterly)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdata = mdata[['realgdp','realcons','realinv']]\n",
      "mdata.index = pd.DatetimeIndex(quarterly)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdata_orig = mdata.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdata.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x113a9e1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mzZM33nhDpk2bJiIiISEhMmHCBBEROXjwoPj4+EhGRobExMSIu7u7mEwmERFp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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111b53750>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.log(mdata).diff().dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['realgdp_div5'] = (1/5.0)*data['realgdp']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.plot(subplots=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([<matplotlib.axes.AxesSubplot object at 0x113ab70d0>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0x113b45ed0>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0x113bcba10>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0x113beae50>], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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50d7ennv27KGtrS0jIyNJFv7x9PT02KZNGx45coQDBw7k3LlzP+g+KgrZ2dl0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\n6BBERERERP5BdAgiIiIiIgCA/wf+a6f3vddWewAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113ab4750>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# make a VAR model\n",
      "model = VAR(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = model.fit(2, verbose=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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z84Ho0CGyev77364/n4bm2DG6N2/epLJEALkEn3iCLGp797ruu06dIm8HYD8O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\nGRlszpw5jDHGDh06xOLj41l9fT0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       "text": [
        "<matplotlib.figure.Figure at 0x113c7d090>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# autocorrelation function\n",
      "results.plot_acorr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eeuopPf/882ltEEhlosyQUUw1MgrbkVHYLvACGzRCjXTlOjNkFOkio7AdGYXt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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113c83790>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Matrix is not positive definite",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-18-19e220c58bbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/var_model.pyc\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1252\u001b[0m         \u001b[0msummary\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mVARSummary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m         \"\"\"\n\u001b[0;32m-> 1254\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mVARSummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mirf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_decomp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/output.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, estimator)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/output.pyc\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self, endog_names, exog_names)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0;34m>>\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_header_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m         \u001b[0;32mprint\u001b[0m \u001b[0;34m>>\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stats_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0;34m>>\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coef_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0;34m>>\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_resid_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/output.pyc\u001b[0m in \u001b[0;36m_stats_table\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    124\u001b[0m                        \u001b[0;34m'FPE:'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m                        'Det(Omega_mle):')\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mpart2Ldata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneqs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnobs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maic\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m         \u001b[0mpart2Rdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbic\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhqic\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpe\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetomega\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mpart2Lheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tools/decorators.pyc\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, type)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_cachedval\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0;31m# Call the \"fget\" function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m             \u001b[0m_cachedval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m             \u001b[0;31m# Set the attribute in obj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;31m#            print \"Setting %s in cache to %s\" % (name, _cachedval)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/var_model.pyc\u001b[0m in \u001b[0;36mllf\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1005\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mllf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m         \u001b[0;34m\"Compute VAR(p) loglikelihood\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvar_loglike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma_u_mle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/var_model.pyc\u001b[0m in \u001b[0;36mvar_loglike\u001b[0;34m(resid, omega, nobs)\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;31m\\\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mln\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m|\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mOmega\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m|\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mK\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mln\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mK\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \"\"\"\n\u001b[0;32m--> 265\u001b[0;31m     \u001b[0mlogdet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m     \u001b[0mneqs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0mpart1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnobs\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mneqs\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/tmarthal/workspace/py/statsmodels/statsmodels/tsa/vector_ar/util.pyc\u001b[0m in \u001b[0;36mget_logdet\u001b[0;34m(m)\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlogdet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Matrix is not positive definite\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mlogdet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Matrix is singular\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: Matrix is not positive definite"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.is_stable()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.model.neqs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.model.nobs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "200"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Non-linear dependent correlations\n",
      "# Log the original data\n",
      "data0 = np.log(mdata_orig).diff().dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results0 = VAR(data0).fit(2, verbose=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results0.plot_acorr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dd98dy5Yti29961sTHQdcgkxCscgkVM6418B2dnbGu+++e9Hbv/SlL11wv1Qq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\n0tL2LTKTOQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114104e90>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results0.is_stable()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results0.coefs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "array([[[ -2.68687246e-01,   6.75015752e-01,   3.32194508e-02,\n",
        "          -5.37374492e-02],\n",
        "        [ -9.66038251e-02,   2.68639553e-01,   2.57387265e-02,\n",
        "          -1.93207650e-02],\n",
        "        [ -1.89516699e+00,   4.41416233e+00,   2.25478953e-01,\n",
        "          -3.79033399e-01],\n",
        "        [ -5.37374492e-02,   1.35003150e-01,   6.64389016e-03,\n",
        "          -1.07474898e-02]],\n",
        "\n",
        "       [[  7.90488934e-03,   2.90457628e-01,  -7.32090753e-03,\n",
        "           1.58097787e-03],\n",
        "        [ -1.18436469e-01,   2.32499436e-01,   2.35037610e-02,\n",
        "          -2.36872938e-02],\n",
        "        [  3.66140240e-01,   8.00280918e-01,  -1.24079062e-01,\n",
        "           7.32280479e-02],\n",
        "        [  1.58097787e-03,   5.80915256e-02,  -1.46418151e-03,\n",
        "           3.16195574e-04]]])"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.tsa.vector_ar.util as util\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A_var1 = util.comp_matrix(results.coefs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A_var1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "array([[ -2.68687246e-01,   6.75015752e-01,   3.32194508e-02,\n",
        "         -5.37374492e-02,   7.90488934e-03,   2.90457628e-01,\n",
        "         -7.32090753e-03,   1.58097787e-03],\n",
        "       [ -9.66038251e-02,   2.68639553e-01,   2.57387265e-02,\n",
        "         -1.93207650e-02,  -1.18436469e-01,   2.32499436e-01,\n",
        "          2.35037610e-02,  -2.36872938e-02],\n",
        "       [ -1.89516699e+00,   4.41416233e+00,   2.25478953e-01,\n",
        "         -3.79033399e-01,   3.66140240e-01,   8.00280918e-01,\n",
        "         -1.24079062e-01,   7.32280479e-02],\n",
        "       [ -5.37374492e-02,   1.35003150e-01,   6.64389016e-03,\n",
        "         -1.07474898e-02,   1.58097787e-03,   5.80915256e-02,\n",
        "         -1.46418151e-03,   3.16195574e-04],\n",
        "       [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00],\n",
        "       [  0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00],\n",
        "       [  0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00],\n",
        "       [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00]])"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eigs = np.linalg.eigvals(A_var1)\n",
      "eigs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "array([  6.14450017e-01 +0.00000000e+00j,\n",
        "        -2.52270365e-01 +9.86656397e-02j,\n",
        "        -2.52270365e-01 -9.86656397e-02j,\n",
        "        -6.51542989e-02 +2.77573117e-01j,\n",
        "        -6.51542989e-02 -2.77573117e-01j,\n",
        "         2.35083080e-01 +0.00000000e+00j,\n",
        "        -1.16591891e-17 +1.98918961e-09j,  -1.16591891e-17 -1.98918961e-09j])"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Eigenvalues\n",
      "np.abs(eigs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "array([  6.14450017e-01,   2.70878654e-01,   2.70878654e-01,\n",
        "         2.85117376e-01,   2.85117376e-01,   2.35083080e-01,\n",
        "         1.98918961e-09,   1.98918961e-09])"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}