Time_series_data_analysis _9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 季節性とマクロ変数を加える？\n",
    "\n",
    "今まで見てきた時間トレンド、ランダムウォークモデル、ARモデルなどの線形のモデルを統一的に扱うことができるのだろうか？　そしてそれに季節性、マクロ変数も加えることができるのだろうか？　もちろん可能である。\n",
    "そして、例えば、 　\n",
    "\n",
    "時系列データ = 時間トレンド＋ランダムウォーク＋ AR（1）＋ 季節性 ＋マクロ変数 \n",
    "\n",
    "と書き、システム的な方法を取ることでより豊かな表現が可能になる。\n",
    "\n",
    "特に季節性とマクロ変数との関係は金融市場の効率性とも密接な関係にあることから長い研究の歴史がある。\n",
    "\n",
    "ただし、同時に変数の数を増やすことで、問題も生じてくる。本章では季節性とマクロ変数の分析を簡単に紹介します。\n",
    "\n",
    "その前に\n",
    "\n",
    "## ニュートンも解けない3体問題を知る 　\n",
    "\n",
    "$$P_{t} = T_{t} + W_{t} + Z_{t} +  \\xi _{t}$$\n",
    "\n",
    "$P_{t}$は観測値　$\\xi _{t}$はかく乱項 そして、それぞれのシステムは 　\n",
    "\n",
    "\n",
    "$Y_{t} = a +  \\beta _{t} + u{t}$　\n",
    "\n",
    "$W{t} = a + W{t-1} + w{t}$　\n",
    "\n",
    "$Z{t} = a +  \\beta Z{t-1} + z{t}$\n",
    "\n",
    "と表現できます。\n",
    "\n",
    "上から、時間トレンド、ランダムウォーク、AR（１）モデルである。\n",
    "\n",
    "それぞれの方程式がかく乱項をもつことで、観測値の複雑な挙動を説明できる柔軟性を確保している。\n",
    "\n",
    "そしてこれらのかく乱項をさらに別の方程式で説明することで、さらなる豊かさが確保される。\n",
    "\n",
    "季節性とマクロ変数の関係をシステムモデルとして加えてもいいし、残差項を季節性とマクロ変数などで説明しても構わない。\n",
    "\n",
    "また、それぞれのシステムモデルを動的・静的に扱う選択も可能です。\n",
    "\n",
    "一般に\n",
    "\n",
    "\n",
    "１．データに多くのノイズが含まれていないか？ 　\n",
    "\n",
    "２．価格の測定単位としての通貨の価値が安定であるか？ 　\n",
    "\n",
    "３．モデルは間違っていないか？ 　\n",
    "\n",
    "４．残差項が多すぎないか？ 　\n",
    "\n",
    "５．現象そのものがランダムではないか？ 　\n",
    "\n",
    "などに十分な注意を払うべきです。 　\n",
    "\n",
    "\n",
    "実は高度な数学、統計学を用いても、正確に金融市場の価格の動きを説明できる可能性は少ないです。\n",
    "\n",
    "現在の金融のモデル化の手法は価格の動きを運動として方程式を立て、それを積分して解を導くという手法を取っている。\n",
    "\n",
    "しかし、このような手法はニュートン力学の延長線上にあります。\n",
    "\n",
    "２つの天体の間の運動方程式を積分により解くことで安定周期を得るニュートン力学では天体の数が３つ以上になるとその相互作用はかなり複雑になり解析的に解を得ることはできなくなる。\n",
    "\n",
    "それを３体問題とよび、変数が増えると解析的には解けないという問題が生じる\n",
    "\n",
    "##  季節性の分析 　\n",
    " \n",
    " 金融市場の季節性は、学術的に積極的に検証されているものから格言的なものまで多数あります。ここでそれらを整理をしておきます。 \n",
    " \n",
    " \n",
    " ＊月次効果 　\n",
    "\n",
    "ａ．ヘッジファンドの決算売り（５月） 　\n",
    " \n",
    " ｂ．米国ファンドの決算売り（６月） 　\n",
    "\n",
    "ｃ．夏枯れ相場（７、８月） 　\n",
    "\n",
    "ｄ．米国の節税売り（10月） 　\n",
    "\n",
    "ｅ．ヘッジファンドの決算売り（11月） 　\n",
    "\n",
    "ｆ．12月の節税売り（12月） \n",
    " \n",
    " \n",
    " ＊曜日効果 　\n",
    " \n",
    " ａ．月曜効果 　\n",
    "\n",
    "ｂ．金曜効果 　\n",
    "\n",
    "ｃ．週末効果 \n",
    " \n",
    " \n",
    " ＊月末効果 　\n",
    " \n",
    " ａ．節分天井彼岸底 ： ３月末の本決算を前に機関投資家、年金などが利益確定売りに出るため。 　\n",
    " \n",
    " ｂ．掉尾の一振 年末にかけて株価が上昇すること。 　\n",
    " \n",
    " ｃ．サンタクロースラリー クリスマスから年末にかけて株価が上がること。 　\n",
    " \n",
    " ｄ．もちつき相場 ： 年末のボラティリティーが上がること\n",
    " 　\n",
    "  \n",
    "  月末の期間をどの程度の長さに取るかによって、月末効果の評価は大きく変わる。また、月末効果は標本の数が少ないので、結果の解釈には注意が必要である。 \n",
    "  \n",
    "  \n",
    "  ＊週次効果 　\n",
    " \n",
    " ａ．第１金曜：米国雇用統計 　\n",
    " \n",
    " ｂ．第２金曜：先物SQ \n",
    "  \n",
    "  \n",
    "  ＊祝日相場 　\n",
    " \n",
    " ａ．ゴールデンウイーク相場：昭和の日の前日からゴールデンウイーク明けにかけて相場が上昇すること。 　\n",
    " \n",
    " ｂ．盆休み相場 ：８月10日前後にポジションを調整する動き。 　\n",
    " \n",
    " ｃ．ラマダン相場 ：ラマダン期間中は相場は横ばいか下げやすい\n",
    "  \n",
    "\n",
    "##  平均値の検定 　\n",
    " 得られた実現値の平均値がその母集団の平均値と等しいか否かを検定する方法を考えてみよう。\n",
    " \n",
    " 母標準偏差の値は未知である。しかし実現値から算出した標本標準偏差と母集団の標準偏差が等しいとすることで、仮説検定が季節性の判別に使えますね。\n",
    " \n",
    " 例えば、バブル崩壊前の1989年１月の価格から計算した終値の変化率の母平均について判定してみます。 　\n",
    "   \n",
    " 仮説検定では２つの仮説を立てる。ひとつは証明したい仮説の反対を示すもので帰無仮説（Null Hypothesis）といい$H_{0}$と書きます。 \n",
    " \n",
    " 帰無仮説は棄却を前提に立てられる。 もう一方は対立仮説で帰無仮説に反する仮説で$H_{1}$と書き\n",
    " \n",
    " 例えば、１月効果ではその効果の棄却を前提に帰無仮説を立てます。\n",
    " \n",
    " 母集団の平均値$\\mu $が想定される値$\\mu _{0} $に等しいとします。\n",
    " \n",
    " そして、対立仮説では母平均$\\mu $は$\\mu _{0} = 0$より大きいと一般には３つの基本形があります。 \n",
    "   \n",
    "   \n",
    "１． 母平均はプラス 　\n",
    "   \n",
    "  $a. H_{0} :  \\mu  = 0$　\n",
    "  \n",
    "  $b. H_{1} :  \\mu > 0 $\n",
    "   \n",
    "   \n",
    " ２．母平均はマイナス 　\n",
    "  \n",
    "  $a. H_{0} :  \\mu  = 0$　\n",
    "  \n",
    "  $b. H_{1} :  \\mu < 0 $\n",
    "\n",
    "   \n",
    "   \n",
    "   ３．母平均はゼロ 　\n",
    "   \n",
    "   $a. H_{0} :  \\mu  = 0$　\n",
    "   \n",
    "   $b. H_{1} :  \\mu \\neq  0 $\n",
    "  \n",
    "    \n",
    " 　これらの仮説が実際の観察値と整合性があるかどうかの判定は、統計的仮説の検定（The Test of Significant Appoarch）を用いて行います。\n",
    "  \n",
    "  統計量Tは\n",
    "  \n",
    "  $$T =  \\frac{ \\overline{ \\mu } }{ \\frac{8}{ \\sqrt{n} } } $$\n",
    "  \n",
    "  で定義されます。\n",
    "  \n",
    "  そしてその実現値をtで表します。 　\n",
    "  \n",
    "  統計的仮説の検定では実現値tが採択域にいれば帰無仮説を棄却することなく、また棄却域にいれば帰無仮説を棄却すると判断できます。\n",
    "  \n",
    "  表にまとめておきますね。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|平均値             | 仮説のタイプ|帰無仮説     |対立仮説     |決定規則     |\n",
    "|-------------------|-------------|-------------|-------------|-------------|\n",
    "|プラス             |右片側       |μ= 0         |u>0          |t>tx,df      |\n",
    "|マイナス           |左片側       |μ= 0         |u<0          |t<-tx,df     |\n",
    "|ゼロ               |両側         |μ≠ 0        |μ≠ 0        |t>tx/2,df   |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表で示した決定規則が成り立てば、帰無仮説を棄却する。1989年の1月の変化率の標本平均はμ=0.00242、標本標準偏差はs =0.00514、標本数はn=18である。\n",
    "\n",
    "$H_{0} ： \\mu = 0、H_{1} :  \\mu > 0$とすると、帰無仮説が棄却されれば１月の平均値がプラスである可能性があります。\n",
    "\n",
    "$t  \\geq  t{x,n-1}$ならば$H_{0}$を棄却し、 $t  <  t_{x,n-1}$ならば$H_{0}$を採択する。\n",
    "\n",
    "ここでｘは有意水準である。計算してみよう。t=1.949。 $t_{0.05,17} = 1.74$であるから、$H_{0}$は棄却され、1989年の１月の平均値がプラスの可能性が否定できないです。\n",
    "\n",
    "## 季節性の具体例 　\n",
    "\n",
    "標本から推定した母平均μがゼロに等しいか否かで季節性を検定してみます。\n",
    "\n",
    "その際には、標本標準偏差を用いるので、分散不均一性の問題が生じる。この問題を避けるために１年間毎に分けて実現値を分析し、分析対象期間でどれくらいの割合で、帰無仮説が棄却されるかを見てみます。 　\n",
    "\n",
    "\n",
    "## 月次の季節性 　\n",
    "\n",
    "それぞれの月の間に起こる値動きに明確な特徴があるかどうかを過去にさかのぼり調べてみました。\n",
    "\n",
    "例えば１月効果であれば、1949年５月16日から2017年8月3日までの各年の１月の日々の変化率からその平均値がプラスであるか否か有意水準10％の統計的仮説の検定を用いて判断し、\n",
    "\n",
    "そして、帰無仮説が棄却された年の数で、季節性の判定をしてみました。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24, 15, 14, 9, 14, 10, 9, 14, 16, 9, 14, 15]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import t\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "end='2017/8/3'\n",
    "n225 = web.DataReader(\"NIKKEI225\", 'fred',\"1949/5/16\",end).dropna()\n",
    "develop=n225.loc[:'1989/12/31']\n",
    "reform=n225.loc['1989/12/31':]\n",
    "year=n225.loc['1989']\n",
    "years=[x+1950 for x in range(66)]\n",
    "m=lambda x:x.month\n",
    "count=[0]*12\n",
    "alpha=0.1\n",
    "for i in range(len(years)):\n",
    "    year=n225.loc[str(years[i])]\n",
    "    r=year.pct_change().groupby([m])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(12):\n",
    "        if float(tv.iloc[j])>t0:# and years[i]>=1990:\n",
    "            count[j]+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 2, 2, 4, 2, 2, 1, 5, 1, 3, 4]\n",
      "1.36343031802\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import t\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "m=lambda x:x.month\n",
    "count=[0]*12\n",
    "alpha=0.1\n",
    "for i in range(len(years)):\n",
    "    year=n225.ix[str(years[i])]\n",
    "    r=year.pct_change().groupby([m])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(12):\n",
    "        if float(tv.iloc[j])>t0 and years[i]>=1990:\n",
    "            count[j]+=1\n",
    "print(count)\n",
    "print(t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[24, 15, 14, 9, 14, 10, 9, 14, 16, 9, 14, 15] 全期間の結果\n",
    "\n",
    "[2, 3, 2, 2, 4, 2, 2, 1, 5, 1, 3, 4] 1990年以降の結果\n",
    "\n",
    "結果は左から順に１月、２月、…、12月となる。平均値がプラスであると判定された月は1950年から2017年までの67年間で１月では24回あります。\n",
    "\n",
    "その内1990年以降に起きたのは２回であり、１月効果はバブル崩壊前の現象であった可能性が高い。39年間の内に22回起きているので、１月にプラスになる確率は高い。\n",
    "\n",
    "バブル崩壊以降は、どの月においても明確な方向性はなく、多くの月で平均値ゼロと判断される。\n",
    "\n",
    "### プログラムコードの解説：無名関数ラムダ 月次、還次のデータの抽出には無名関数lambdaを用いる。\n",
    "\n",
    "月の最初の日の変化率はその前の月の最終日の価格があれば、 それを用いて計算する。\n",
    "\n",
    "データが月の初めから始まれば、最初の日の変化率はNaNが返される。\n",
    "\n",
    "y=lambda x:x,month 無名関数による月の設定 r=year.pct_change().groupby([y]) グループ化\n",
    "\n",
    "### プログラムコードの解説：年次データの取得 for i in range(len(year)): year=n225.ix[str(year[i]) 1年間のデータの抽出\n",
    "\n",
    "プログラムコードの解説：月次の推定量からt値の算出 from scipy.stats import t ステューデントの1分布のインポート \n",
    "\n",
    "tv=r.mean()/r.std()*np.sqrt(r.count()) t値の算出 t0=t.ppf(0.90,len(r)-1) t分布の臨界値 for j in range(12): 月の選択 If float(tv.iloc[j])>t0: 判定 count+=1 H0 棄却数\n",
    "\n",
    "また、同様の分析をマイナスの変化率に対しても行ってみました。その結果は\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 5, 2, 9, 4, 6, 6, 5, 4, 7, 3]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import t\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as pdr\n",
    "import numpy as np\n",
    "m=lambda x:x.month\n",
    "count=[0]*12\n",
    "for i in range(len(years)):\n",
    "    year=n225.ix[str(years[i])]\n",
    "    r=year.pct_change().groupby([m])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=-t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(12):\n",
    "        if float(tv.iloc[j])<t0:# and years[i]>=1990:\n",
    "            count[j]+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 2, 0, 4, 2, 2, 2, 3, 0, 3, 1]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import t\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as pdr\n",
    "import numpy as np\n",
    "m=lambda x:x.month\n",
    "count=[0]*12\n",
    "for i in range(len(years)):\n",
    "    year=n225.ix[str(years[i])]\n",
    "    r=year.pct_change().groupby([m])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=-t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(12):\n",
    "        if float(tv.iloc[j])<t0 and years[i]>=1990:\n",
    "            count[j]+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[2, 5, 5, 2, 9, 4, 6, 6, 5, 4, 7, 3]全期間の結果\n",
    "\n",
    "[1, 2, 2, 0, 4, 2, 2, 2, 3, 0, 3, 1]1990年以降の結果\n",
    "\n",
    "です。変化率がプラスの検定の場合と同様に、左端が１月であり右端が12月である。１月効果のような現象は見られない。\n",
    "\n",
    "12月の節税売りも見られない。あえていえば５月のヘッジファンド売りがあるかもしれないが５月は収益率がプラスになる可能性も高い\n",
    "\n",
    "## 曜日の季節性 　\n",
    "\n",
    "次に曜日について分析してみました。月曜効果、金曜効果、週末効果が注目されるが、本分析では金曜の終値から月曜の終値までの分析となるので、月曜効果と週末効果が複合して分析されている\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15, 17, 14, 13, 10]\n"
     ]
    }
   ],
   "source": [
    "w=lambda x:x.week\n",
    "count=[0]*5\n",
    "for i in range(len(years)):\n",
    "    year=n225.ix[str(years[i])]\n",
    "    r=year.pct_change().groupby([w])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(5):\n",
    "        if float(tv.iloc[j])>t0:\n",
    "            count[j]+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "65年間で月曜が15回、火曜が17回、水曜が14回、木曜が13回、金曜が10回の平均値が有意にプラスであると判断された。\n",
    "\n",
    "月次効果と同じように、ほとんどの有意な曜日をもつ年はバブル崩壊前に起きていて、バブル崩壊後には曜日には全く特徴が見られない。 \n",
    "\n",
    "### プログラムコードの解説：lambda関数、曜日の設定\n",
    " \n",
    "python\n",
    "y=lambda x:x,weekday 無形関数による曜日の設定\n",
    "\n",
    "\n",
    "変化率のマイナス側の結果は次の通りである"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 10, 5, 3, 7]\n"
     ]
    }
   ],
   "source": [
    "w=lambda x:x.week\n",
    "count=[0]*5\n",
    "for i in range(len(years)):\n",
    "    year=n225.ix[str(years[i])]\n",
    "    r=year.pct_change().groupby([w])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=-t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(5):\n",
    "        if float(tv.iloc[j])<t0:\n",
    "            count[j]+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "w=lambda x:x.week\n",
    "count=[0]*5\n",
    "for i in range(len(years)):\n",
    "    year=n225.ix[str(years[i])]\n",
    "    r=year.pct_change().groupby([w])\n",
    "    tv=r.mean()/r.std()*np.sqrt(r.count())\n",
    "    t0=t.ppf(1-alpha,len(r)-1)\n",
    "    for j in range(5):\n",
    "        if float(tv.iloc[j])>t0 and years[j]>1990:\n",
    "            count[j]+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[7, 10, 5, 3, 7]全期間の結果\n",
    "\n",
    "[0, 0, 0, 0, 0]1990年以降の結果\n",
    "\n",
    "特に注目する結果は見られない\n",
    "\n",
    "## マクロ変数との関係（単回帰と多変量解析） 　\n",
    " \n",
    " 今まで、内閣府の景気循環期をもとに日経平均株価を分析してきた。\n",
    " \n",
    " ここでマクロ変数と日経平均株価の関係を考えてみよう。\n",
    " \n",
    " 誰にとってもなじみの深い経済変数は、外国為替レート、国内総生産、金利、物価水準、労働人口、マネーサプライなどではないだろうか？　\n",
    " \n",
    " ここではFred（セントルイス連銀サイト）からできるだけ長い期間のデータでダウンロードできる変数を３つ選びました。\n",
    " \n",
    " 1つは労働人口、国内総生産、そしてドル円の為替レートです。\n",
    " \n",
    " 国内総生産（出所：OECD）の指数はドル建てであるために、円建てに直し\n",
    " \n",
    " ドル円の為替レートはドル建ての指数を円建てに変換するために用いた。日経平均株価を労働人口と国内総生産の国内要因のみで説明するモデルを考えてみました。\n",
    " \n",
    " 説明変数と被説明変数の変数すべてに対数をとるので、モデルは対数線形モデルとよばれます。\n",
    " \n",
    " この場合にはある説明変数の傾きの係数はその説明変数に対する被説明変数の弾力性を表しています。\n",
    " \n",
    "## 単回帰分析 　\n",
    "  \n",
    " 日経平均を分析する前に手始めとして、労働人口と国内総生産について調べてみました。\n",
    " \n",
    " 労働人口は経済の原動力であり、国内総生産はその結果です\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "start='1971/12/1'\n",
    "end='2017/8/3'\n",
    "workpop = web.DataReader('LFWA64TTJPM647S',\"fred\",start,end).dropna()\n",
    "gdp = web.DataReader('MKTGDPJPA646NWDB',\"fred\",start,end).dropna()\n",
    "gdp=gdp.resample('A',loffset='-1d').last().dropna()\n",
    "fx = web.DataReader('DEXJPUS',\"fred\",start,end).dropna()\n",
    "fx=fx.resample('A',loffset='-1d').last().dropna()\n",
    "workpop=workpop['1972':].resample('A',loffset='-1d').last().dropna()\n",
    "gdpjpy=gdp.MKTGDPJPA646NWDB*fx.DEXJPUS\n",
    "gdpjpy=np.log(gdpjpy).dropna()\n",
    "workpop=np.log(workpop).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 3.379e+27\n",
      "Date:                Fri, 04 Aug 2017   Prob (F-statistic):               0.00\n",
      "Time:                        01:53:25   Log-Likelihood:                 1311.3\n",
      "No. Observations:                  45   AIC:                            -2619.\n",
      "Df Residuals:                      43   BIC:                            -2615.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       3.411e-13   5.77e-13      0.591      0.558   -8.23e-13     1.5e-12\n",
      "0              1.0000   1.72e-14   5.81e+13      0.000       1.000       1.000\n",
      "==============================================================================\n",
      "Omnibus:                       14.676   Durbin-Watson:                   0.001\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               16.013\n",
      "Skew:                          -1.378   Prob(JB):                     0.000333\n",
      "Kurtosis:                       3.971   Cond. No.                     2.37e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.37e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "x=sm.add_constant(gdpjpy)\n",
    "model=sm.OLS(gdpjpy,x)\n",
    "results=model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ドル建てのGDPは円換算して対数をとっています。\n",
    " \n",
    " これを被説明変数、労働人口を説明変数として回帰を行い、GDPのデータは年次で、他のデータは日次、月次であるために、GDPのデータに期間を合わせています。\n",
    " \n",
    " 切片、回帰係数ともにｐ-値がゼロとなっています。\n",
    " \n",
    " $R^{2}$も0.586と高い数値となりました。\n",
    " \n",
    " つぎに期待値(fitted)と実現値をグラフで比べてみます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcf7c916f60>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VzR9208VfDL1Mx4KMzWbXXl+u0vffeFpXfTLgUvv2pbN1+7olGnY6RhOSbFc19vovh2jo\nO431m5nz9NybFXTttPfS7QfEahbfr0AAsCvV9Xn+S00qwIUM5NxxOF2+mM49N+AU4JfJc5fjiHbI\nFbuR74YrL46Cahxzi0OHDmn9+vWzJZNiZLPiev65pWXpnlPaZPQ/GvjGQp216Uiuf6mGHj2h1V+d\nr2/+vjPHxkxKTNBf3u6n70+ekWNjpmC323XjoSgdPnubBr6xUKuOnK+dPlqqXyzer7EJSXrhYqI2\ne+dfffmrGWq3Xd2XeGbs2fCv7hndTA/ud+7nlZyUpAmJSaqqejxsr8ZGn8txnXKSn9cd1Koj5+uc\nLUdzfOxtS2fp2FGPaMDIP3TMzKUZ/vtcpXHcA3S2zm8BNqcjIziceD7LYMyuwIo0bb6Aq3VeHUeY\nX+ms9LvawyyrGgxpSLLZ+WDhXh76cRMVShThz2c7cG8L/1zfh70w/WGmun/Is11yLoTGzd2D7Y1e\nZ2pYSS4mXn1c7P7dW4iOjbusTURoGVCaj+5pzKbXb2XcvY2pVKII4xbt59npW/H2cOODhsf5OPIJ\ndq6ce62vchnJNjs/rdiNTYVyFZzbI3N1c8PD3Y2kxASSptxN8Od9iIgumIUHok6F0+bv7jxW+TC9\nm1TO8fEb33wv5e96g/tbBfBy35sQl6szBSIyA1gH1BGRcCvE7jFgnIhsB8ZgZSoTkUoikhKB0B4Y\nCHQRkW3W0S3V0Pdz+ZIqQCdgh7Xn+CswVFXPXJXiTmAccq5DAgIC2LVrV7ZkwsLCckeZQsbxcxd5\ndsZWNh8+ywOtq/DmXYF4ued+HlS120mueQduSfGU9cnZ4PluDSqybMNWNm3eSKer2Eu2JSdT7Nf7\n2FukDi1HpB/e4+3hRt/mfvRt7sdP68J4Y95uxizYw4g7+7H8bAj1GzqfncUZflwbxrSoWnQc8Cf1\nU3niOoO7hydR7d7gkxWnODx+LT8+1JJa5X2yFsxD4i5EkeReggdva5trf5Q92KbqNY+hqv0zuNU8\nnb6XwvVUdTWO2WNG4w5Jp+03HPuTeYIxjgaDxdK9p3hx1naSku180b8pPRtXyrNni4sLLfs8nytj\nt6lWkr+8RnFobQtom/0Z3JK9EUxPGMzQTo2d6j+wbQAHImOZvPoQ1X2LMuCh/2X7mZlx8ugBDi76\nli51+nBHgwpXNUbTW/vzat3zPDxlE1MnvMd9t7ajQYeeOarnteBfqzGMWpffatzQ3NDLqtbatcFJ\nrtefV5LNzvsL9/Dwj0FUtJZR89Iwbls2m+W/TSAp+Uqvz5zAzc2NkHYfU6TrlbFtzjBl/RH2+7Sl\nRafuTsu83r0enev48ua83awOOU3I1pVsHtuLhPi4rIWz4NDfX/KGTOZ/XUpf06yqoV8J5j7RksEu\nfxO36usC8fudkHCR5d+P4sy5c/mtyg3PDWscvby8iIqKKhD/IQoDqo56jl5e11e+zNCIaPpNXMfE\nFQcZ0LoKc59qR3XfYjkytt1mI3T7aiJPp00d/B9JNjsXVk8mYPfXiNpz5Lnp0fr2+wmsn/3EImF7\nt1Lv0FSGtCiLm6vzXxduri582b8pNX2L8eS0zYQcj8Iveht/LV+TbR3S0vrhcZy4dz6Vql773qxf\n2RKUe24J1Z+YViBiexf9MZ3OR77iyLYl+a3KDY/cCMahaNGiGhsbe1lbUlIS4eHhxMcXzA35goiX\nlxd+fn64uzuXPaMgk2SzM2nlQT5fHIK3pyvv9mpAjxyYLSYlJqC44OHhzvoZ79Fm30e0if+SEuUD\n6FPpDG18E6nboReeHo4SVD+tC+OteTv5qV8V2jdzbtnyatm74V8uHN9Hq7ufdVpm/c9v0zTkK+Ke\n2Ukp34rZfubRM3Hc/fUainq4Ubm4sDcyiRXDO2ea3zMjzp4+STKu+Jb1zbrzVXDhXBT7v3+CCj3f\nxq9mg1x5RmYciYrjtk9X8GD1WN54+J48f356iEicqhbNbz3ygxt2zzElQ43hxmP38fOM+HUHu49f\noFvDCozu2QBfn+zVS0xNQrKN1SGn2bFpJQ8dHEZwu89od/u9VOt4Pxu9SzO4RHvWhJ6m+O6fqC5r\nSGzTA08P2Ld6LrOWxtKqeg3aNc3dLDIAF9b/SLUza0jo/gSeGdQ4TUubB9/mVPhAyl+FYQTwL+3N\nxIEt6P/teop6FeNMbAxz/vqLwX17Zy1sERWTwOTVh7h93UDKyVnOPLOW0mXLZS2YTWLOnSbgwka+\nmzOX54bVxdsj774e1W5n3G/LcXNx4bG+d+XZcw0Zc8POHA03HgnJNsYvDeXr5Qco6e3Bu73qc2fD\nq/vST2H9tHf4c/9FpiV0oKyXnfElfqZ452eo17TjFX3j42II27+Duk0c2bT2vdeGIomnOT9oGQ1r\nZF1K6FpZu203j/yyly8HdeDWwKwL4CYn23BzyxlP3T+2H+e5GVtpUyScGfYR7O23knr1G5MQH4en\nV/op3U4fP0zwn5/xdPgtxCQLz9aIpFfrutRo2CZHdEqPNcFhDPxpN90bVeKL+5s4tdQatGI+JVa9\nTVzn0TTu4Py+bGq2/P0jgete5t9W39Oze8FxDDIzR4PhOmfrkbOM+HUHIREx9GlamTfuCqRUUedm\nTxmREB9HmYPz6Fi8Mbfd1ZJ2Ncri4dYjw/5e3sUuGUaAco/P5cLp43liGAFaNqyHx7xjLNh1Ikvj\naLfZODCmFSdq3EPnAa9e87N7Nq7EwcgYPlsML1T6mg9qOZYtt333NCXP7OCvNtO4q3FlaqcKqTi2\nbyPtjv/I0OqNuaP7PdQslzN7wZnRPjCAl+9IZuk/81gti+l4//AM+6oqXy0N5cfF0fzhcQbvao7Z\nf2JCfLZrWVZu0JFtYf3odvu15dM15Bxm5mi47pmx8Qij5u6kfHEvxtzdkJvr5tySXMyFs3h6eePu\ncfXLsnnJhB+nUDdsKu1e+TPDGRvAqh0hnJ39LBXb3kfLbg/lyLNVlRdmbuP3bccpW8yDfi38aXBh\nJbFHtzPidDdUYVLRibj6t+CWIW+idjunwg9QoUqtHHl+dvTcPO5uysSEUH7ERry9r5w4xUafY8GU\njxge3o67m/rxfp+GeLm7onY7Oz66nWjPitR4eBIVSxTJ9FnJSYkkxMdRNJuxmnmFmTkaDNcpy/dF\n8Prvu+hYy5cvH2jqdCmezNi9dgFR636mxVOTKVa8VA5omXe0q1KEUmHhhIftp0bdjL1Xvws6yx6v\nl1lzR85VCxIRPunXhF5NKzNt/WG+WXEApTI312nKuJsrEnMxHt/VCcTaYhz9XVzy3DCm6Fn38R+I\nS0hK1zACBC+aQp/Ir/Fo34qedzW+tPxqt9uJK9uYJYdtPDZ2BU91rsFj7f3w8nIYydjo8+zft4sV\n53wJCjvLC0efI6lIWQIGT6RCxWuuhmHIQXJt5igiXsBKwBOHEf5VVd8SkXeBXoAdiACGWJkTUsvW\nAWamaqoOvKmqn4nI2zjSE0Va915TR2HkDDEzxxuTvScvcM+EdfiX9mb20LY5UkUj+PgFFk58lXtc\nV1Di6aWULJPzjiG5ic1mQ5VM9xI3/TmJ59Z4cf+tbXn+1twzTsfOXeSXjUf4ZdNRIqMTqFyyCA+0\nrsKA1lUo6X1tS945hS05maDfxtK457N4FSlKdPQFfHyKo3Y7B3etp0aj9KsxHT0Tx5gFe4gN/ocP\nPH8g7t6Z1AxsRtAnffE7v5m2iV9Rt0IJBpfYRk1fLxrfNqhArj7cyDPH3DSOAhRV1RgRcQdWA88D\nwWqVJhGR54BAVR2ayTiuOBLMtlbVw5ZxjFHVsc7qYozjjUfEhXh6j1+DTZXfn26f5fKWMxw9E0ef\nCWtxcxHmPNqEir5lckDT/EHtdkeMr7gQsmMdZ8P30eYux/Lpvv+1oqjtPJ7PrMW3TO6/Y5LNzr+7\nT/Hz+sOsOxhF2WKe/K93fbo2uDZnqZwgeP3fBP59H79Ufo3KJb2osftLkh/6lypVqzslv3Pt3ySs\n/Izaz/xK8WLF2Be0lIToMwS06UHxIgXPGKblRjaOubasamV0j7Eu3a1D9fKaXUVxVI3OjFuAA6p6\nOOe1NFyPxCUm8+jUIM5dTGLWE21zxDBGnQrn1MQHqGR/iLFP3kNF34KVizM7HN63Da9f+vBD2eHM\nPlOT1xI/p7PLduJvfxAvD3dKPTST0uUq4+aeN7M3d1cXujeqSPdGFdl17Dwjf9vB0J+30L1RRd7p\nWZ8yxfLPiAS26cpPx37kjU0e1JPDvF26AYElnd8fbNiuK7T7z8mmTot8KWpvuApydc/RmvVtBmoC\n41V1g9X+HjAIR92vm7MYJr3s7M+KyCAgCHhJVc/mqOKGQovNrgz7ZRu7jp1n0sAWNKhcIkfGvXD6\nGJX0JB929y9wSaqzS4WqtTmnytKo0nSsXRYf/zehVmW8PBz7seUq51/8b4PKJfj96fZ8s/wAXywN\nYd2BKN7pVZ/uDSvmWwabB+7uzX7X3VQoUYeWNz2Ji0v+Z9Ix5D554q0qIiWBucCzqrorVfurgJeq\nppv0UUQ8gONAfVU9ZbWVB07jmHG+C1RU1YfTkX0cq1SKh4dH84SEhJx9KUOB5L2/gvl21SHevCuQ\nhztc+5d8bPQ53LyK4unuTlJiQoHcF7oaEuLjcHf3xMU19yuOXC37TkYz/Nft7Ag/T9f6FXi397Ul\nazBknxt5WTVPcquq6jlgGY4ClqmZBvTNRPROYEuKYbTGOqWqNlW1A98CrTJ45iRVbaGqLdzcjFPu\njcDP6w/z7apDDG5blYfaB1zTWKrK8o1biBnXjDUzPwG4bgwjgKeXd4E2jAB1Kvgw58l2jOxal6X7\nIrjt0xXM23Ysv9Uy3CDkmnEUEV9rxoiIFAFuA/aKSGr3t17A3kyG6U+aJVURSb1LfzeQvcKFhuuS\nFfsjeeuP3dxcx5c37gq8bAnu8J7NrP/6cQ7t3ebUWEcizvHwj5sYMuc4Gz1aUalOi9xS25AFbq4u\nPNm5Bgue60C1skV5/pdtfLJovykYYMh1cnPmWBFYJiI7gE3AIlWdD3wgIrus9ttxeLCmrRKNiBTF\nYVDnpBn3IxHZacnfDLyQi+9gKASERsTw9LQt1C7vw5cPNLuseoSqMuHfbbSJmMnZc46t6dPHD3Mq\nPDTdsTb9Ph4Z35I9h47yevdA7hw5nbotb8mT9zBkTM1yPvw6tB33NvfjiyUhjPvXGEhD7pKb3qo7\ngKbptKe7jJq6SrR1HQtc4UeuqgNzUE1DIedCfBKP/xSEp5sLkwe3uCKW8e9dJ/nlRAUCu61jQMu6\nAIT+8SFNT8ziMb9Z9GxVl9sCy+PuIri6ulAyoBGRofWYN6Al5StWyY9XMmSAq4vwYd9GuLoIXy0L\nxabKiDvqFIhSU4brjxu2nqOh8GO3Ky/O3MaRqDi+HtCMSiUvD9lITIhnz5+fUr+cBwM61MPVmlH6\n3/4cS2q/we7TyrMztrL8f91YMeEZAGo16Uizl/8whrGA4uIijLm7IQ+0rsKE5Qd4f+FeM4PMR0Tk\nexGJEJHUjpZNRGS9iGwTkSARucIvRET8RWSZiASLyG4ReT7VvbdF5Jglv01EuqW696qIhIrIPhG5\nIzffzXiqGAotXywNYfGeCN7uEUjr6lcGq+9e8SsvJk7klratcE3lfl+5el0qV6/LHXZlbWgkbgvL\no8VKoapmFlIIcHER3uvdADcXYdLKg9jsyuvd65l/u/zhR+ArYGqqto+A0aq60DJsHwGd08gl4wjD\n2yIiPsBmEVmkqsHW/U/TJnoRkUAcoX31gUrAYhGpraq2nH4pMMbRUEhZFHyKzxaH0LeZH4PbBaTb\np+ntD7K/rD+NmlxZPgocy3Qda5eD2lPTvW8ouIgIo3vWx0WEyasPYbMrb/UINAYyj1HVlSISkLYZ\nKG6dl8ARjpdW7gRwwjqPFpE9QGUgOG3fVPQCflHVBOCQiITiiFZYdy3vkBHGOBoKHaERMbwwcxsN\nK5fgvbsbpPuFGBMXRzFvb2o3uykfNDTkBSLCWz0CcXVxGEi7KqN71jcGMmdxE5GgVNeTVHVSFjLD\ngH9EZCyOrbv0E9BaWMa1KbAhVXN6iV4qA+tT9Qm32nIFs+doKFRExyfxhOWA883A5ni5Xxmrd/Jo\nKPEf1WXdnM1kAAAgAElEQVT9wp/zQUNDXiIivN69Hk90qs7UdYd5c95usweZsySnxItbR1aGEeBJ\n4AVV9ccRTTA5o44iUgz4DRiWKrXoBBzFJprgmF2Ou6Y3uErMzNFQaLDblRdnbScsKo6fH2lN5ZLp\n50z9bkUoLe11aBTYMo81NOQHIsIrd9YFgYkrDlK8iBvD76ib32rdyAzGCtEDZgPfpdfJKkjxGzBN\nVS+F7KVO+iIi3wLzrctjQOrK4H5WW65gZo6GQsNXy0JZFHyKUd3q0bZG+tUigo9fYPJuG1vafEHF\nqnXyWENDfiEivNK1Lg+0rsL4ZQeYtPJAfqt0I3McSNnP6AKEpO1gVW2aDOxR1U/S3Mso0csfwP0i\n4iki1YBawMYc1v0SZuZoKBQs3XuKTxfvp0/Typmmhts8awzVvJrx1M018045Q4FARHi3VwMuXExi\nzIK9lCjizn0tTUhObiIiM3B4opYVkXDgLRz1dj8XETcgHivHtYhUAr5T1W5Ae2AgsFNEUlJXpdTm\n/UhEmuBw7AkDngBQ1d0iMguH004y8HRueapCHiUez29MPcfCzbm4RG79ZAXlfLyY81S7dPcZwVGK\nqdL0LgTVfoG2A97IYy0NBYXEZDuPTQ1iVUgk4x9oxp0N878uZGHlRk48boyjocAzfPZ25mw9xp/P\ndCCwUvFM+x4N2U45/1p4ennnkXaGgkhcYjIDJ29kZ/h5Jg9pQcdavvmtUqHkRjaOZs/RUKBZE3qa\n2ZvDebxT9UwN47nz5wHwr9XYGEYD3h5ufD+4JdV9i/LET5vZcsSUfDVkD2McDQWWi4k2Xpu7k4Ay\n3jx/S60M+yUmxBP3aQuW/fBmHmpnKOiU8HZn6iOt8PXx5KEfNrHvZHR+q2QoRBjjaCiwfLZkP4ej\n4hjTp2GG+4wA6/Ye5a/kFhQPuCLPveEGp5yPFz8/0hovdxcGTt5A2GmzvWJwDmMcDQWSXcfO892q\nQ9zXwp92Ncpm2nfunhjGezxEw4698kg7Q2HCv7Q3Pz3SmiSbnXu+WceeExeyFjLc8BjjaChwJNvs\nvDJnB6W8PXitW71M+16MjSEseCN31i+Ph5v5dTakT+3yPsx6oi1uLsJ9E9ex+bDZgzRkTq59m4iI\nl4hsFJHtVkmS0Vb7uyKywypF8q8V+5KefJhV1Hhb6tx+IlJaRBaJSIj1WSq33sGQP/ywJoxdxy4w\numd9Sni7Z9o3eMVsfncZwQMVT+SRdobCSq3yPswe2pbSRT148LsNrAqJzG+VDAWY3PxTOwHooqqN\nceTI6yoibYCPVbWRqjbBkRYoMy+Km1W1iaq2SNX2CrBEVWsBS6xrw3XCkag4xi3ax631ytOtYYUs\n+1dv2ZWNDd4isNWteaCdobDjX9qbWUPbUrWMNw//uImFO80fVYb0yTXjqA5irEt369BUyWUBiuLI\ngpAdegFTrPMpQO9rUtRQYFBVRv2+EzcXF97t7Vx1hVK+FWl1z4u4uplkTwbnKOfjxczH29LIryRP\nT9/CrKCj+a2SoQCSq5s0IuJqpQaKABap6gar/T0ROQoMIOOZo+IoZrlZRB5P1V7eqgUGcBIon0vq\nG/KYOVuOsSrkNCO71qFiifSTiqdmx+r5rJz3A0lJSXmgneF6ooS3Oz890or2Ncsy4tcdfLfqYH6r\nZChg5KpxVFWbtXzqB7QSkQZW+yirnMk04JkMxDtYsncCT4tIp3TGVzKYeYrI4yISJCJBycnJOfE6\nhlzkdEwC//srmOZVSzGgdVWnZC6u/ZZaW9/DzTXjMA+DISO8Pdz4bnALujWswP/+2sMn/+4z5a4M\nl8gT9z5VPQcsA7qmuTUN6JuBzDHrMwKYi6PiM8CplKzt1mdEBvKTUmqQuZkltwKNqvLqnJ3EJtr4\noE9DXFyyXk49E5vIoHOPMq/xBMTFeKkarg5PN1e+7N+Mfi38+GJpKN+aGaTBIje9VX1FpKR1XgS4\nDdgrIqlTnfQC9qYjW1REfFLOgdu5vGzJYOt8MDAvd97AkFfMCjrKouBTjLijDrXK+zgls3DXCRLs\nLnRs3SrrzgZDJri6CB/0acRdjSoyZsFe5mwJz2+VDAWA3JxSVQSmiIgrDiM8S1Xni8hvIlIHsAOH\ngaFwRTmT8sBcyyHDDZiuqn9b434AzBKRRyz5frn4DoZc5nBULKP/DKZdjTI83L6a03K+K15jaMka\nBFbslovaGW4UXFyEcf0acyY2kRG/7qB0UQ861ymX32oZ8hFTlcOQbyTb7PSbuI7QiBj+HtaJSiWz\ndsIBRy7VQx/fxJlKnWj78Me5rKXhRiI6Pon7Jq4nLCqWGY+1obF/yfxWKV+5katyGONoyDe+XBLC\nuEX7+fz+JvRqUjnb8nabDRfjjGPIYSKi4+k7YS2xCTZ+HdqW6r7F8lulfONGNo7Gk8GQL+wIP8fn\nS0Lo0bjSJcNoszv3h1pcfDyAMYyGXKGcjxdTH26NAIO+30jEhfj8VsmQDxjjaMhRzp0+ScSxQ5n2\nuZhoY9jMbfj6ePK/Xg0AWBt6msaj/80yIPvYwb0kvF+ToMWzckxngyEt1coW5YeHWnImNpHBP2zi\nQryJpU0PEfleRCJEZFeqtiYisj4l9aeIXOE1JyL+IrJMRIKt9KLPp7r3sYjstdKMzk3l2BkgIhet\ncbeJyDe5+W7GOBpyhMOhuwk/eoTkr1pzZObwTPu+v3APByNjGXtvY0p4u6N2O6d/e5maiXv5Y840\nNswel6Hssj3HWGFvSOVajXP6FQyGy2jkV5JvHmxOyKloHp8aREKyLb9VKoj8yJUheh8Bo6049Tet\n67QkAy+paiDQBkcse6B1bxHQQFUbAfuBV1PJHbBSijZR1aE5+B5XYIyj4ZpRVaJnDSXp+ztZGfA8\nb0TdxpGouHT7Lt8XwdR1h3mkQzXa13SUooo4fogOF5cwvNFFni+5htLBU4m7mL78zyHu/FTpDSpW\nrZNr72MwpNCpti8f39uI9QfP8NKs7didXPq/UVDVlcCZtM1Aceu8BHA8HbkTqrrFOo8G9gCVret/\nVTUlc8t6HElk8hwTHW+4Zpbvi+Tj6Pt5qa0P7Tv345UPl/HNygOMubvhZf3OxCYy/Ncd1C5fjOF3\n/GfcyvvV4OLwYFq4uiIyjDNno/Au4o3a7ZcF+B88dIiok0e5v0e7PHs3g+Hupn6cPJ/Ah3/vpWoZ\nb4bfUTe/VSroDAP+EZGxOCZgmf6HFZEAoCmwIZ3bDwMzU11Xs1KSngdeV9VVOaFwepiZo+GasNmV\nD//eS2zpQDp2e5Dyxb0Y0rgIdbf8j8jjYZf6ObLg7OBcXCKf3dcUL3eHM01UVCTJyTaKFPXB08sb\nD08vKlSojN1mY9OXA1k3+WXUbgcgYsmXrPF8lu61nQv5MBhyiqE3Vad/K3/GLzvArE03VKJyt5Q0\nnNbxeNYiPAm8YKUIfQGYnFFHESkG/AYMS1OUAhEZhWP5dZrVdAKoYi3XvghMF5Hi5BLGOBquic1/\nTqTv6QmMvDXgUrHhwS0qcK/LUvZv/PdSvwkrDvDP7lOMuKMugZX++30+PqkfWz/udkVOS1UFtRN0\nKJIP/9mPqlL5psFsb/wWvr5Zl7IyGHISEeGdXg3oWKssr83dyeqQ0/mtUl6RnJKG0zomOSEzGJhj\nnc/mv9SflyEi7jgM4zRVnZPm3hDgLmCAlUMbVU1Q1SjrfDNwAKh9Fe/kFMY4Gq4JW8QeOnkdoGuj\nKpfaKlevy/mndtK+t+OPzGX7Ivj4n330aFyJRzv+lwVny+EzTI9pSmy1O64oT+Xq5kaLZ3/mVPOX\n+GbFAT6eswa/Gg1p2ed5DIb8wN3VhfEDmlHDtxhP/ryZ/aei81ulgspx4CbrvAsQkraDOP7DTwb2\nqOonae51BUYAPVU1LlW7r5VxDRGpDtQCci0ZrkkCYLhmEhPi8fD0Svfe7rDj3P/jTvxLefPbk+0o\n4vFfbOJDP2xk29FzrB7ZhaKe6W9/qypj521g2NZurK7xIjcPej1X3sFgcJZj5y7Se/waPFxdmPt0\nO8r5pP+7fz2QVRIAEZkBdAbKAqeAt4B9wOc4fFrigadUdXPqFKEi0gFYBezEkUoU4DVVXSAioYAn\nEGW1r1fVoSLSF3gHSLJk3lLVP3P2jVO9mzGOhqvh/NnTnDhxjLqBGYdULP35fd7dXZZzXlX449mO\n+Jf2vnRv/44NfPHLn9S75UGeviVzBwe129kwfTRqS6Lt4DE59g4Gw9WyM/w8/Sauo1b5Ysx8vO1l\nf/RdT5gMOQZDNtk9+12qzezC8fD0A/7tduW7s80I0wrcVbfkZYYR4OzKiYzxmMzA5r5ZPktcXGjz\n4GhjGA0FhoZ+Jfiif1N2HjvP879sdTq7k6HwYIyjIducOH+REUdaM6fSS1TyS7+SxhdLQ1h7LJlq\nvj4sDLlAfNLlAdQthk7i7H3zKV6yTF6obDDkOLcFlufNuwL5N/gUYxbsMYWSrzOMcTRkm88WhRCh\npehw77B07/+z+ySfLQ6hbzM/xvRuQIXYvaxeMOPS/YSkJFzd3Khar3leqWww5AoPta/GkHYBTF59\niH4T17Em9LQxktcJxjgassXhPZu5aftLPNmsyBVLpQAhp6J5ceY2GvuV4L27G9CmRlk+KvYL1bZ/\njNrtHN63lYj36rNj/eJ80N5gyHneuCuQd3vV5+iZiwz4bgP3TVzPWmMkCwQiUkFEeopIDxHJVgyY\nMY6GbHH60A6aux5gSKcr07edv5jEY1ODKOLhxjcDm18K9C9y7wR8nlyEuLjw67oQjmlZ/KrXy2vV\nDYZcwdVFGNg2gOXDO/NOr/ocORPHA8ZI5jsi8iiwEegD3AOsF5GHnZbPrX84EfECVuJwyXUDflXV\nt0TkXaAXDlfcCGCIqh5PI+sPTAXK48jTN0lVP7fuvQ08BkRa3V9T1QWZ6WK8VXOW9EI37HblsalB\nrAyJZMZjbWgRUPoKubDTsXQZt4xHOlRnVPfAK+4bDNcD8Uk2ZgUdZfyyUE5dSKBVtdK8fHsdWlW7\n8v9EQacwe6uKyD6gXUriABEpA6xVVacSM2c6cxSR3iLysojccRW6JQBdVLUx0AToKiJtgI9VtZGV\nAmg+jqztacksYzvAp6kys2dqGA3XjtrtrPvuJZb//h1AujGN36w8wJK9EbzePTBdwxh1KpyyX1an\nq2zgsU7Vc11ngyG/8HJ3ZVDbAFYMv5nRPetzOCqWfhPXMXz2ds7FJea3ejcSUUDqTA3R/Bc7mSUZ\nGkcR+RpHXrwywLsi8kZ2tFIHMdalu3Vomvx5RXHMDNPKZpix3ZC3qCrvz9+Ox5EVeB1dle4S0boD\nUYy1MuAMals13XFKla3IEY+a9K+ZdF0HTRsMKXi5uzK4XQDLX76ZoTfVYM7WY9z6yQr+2H7cLLXm\nDaHABhF5W0TewlHhY7+IvCgiL2YlnOGyqlW8srGq2kTEG1ilqtlyL7RS/WwGagLjVXWk1f4eMAhH\nZvWbVTUykzECcCzPNlDVC9ay6kOWbBCOGebZzPQwy6pXh91m4815O/h543Eea12eV3s2w8X18r+n\nIi7E0+2L1ZQo4sa8ZzpQLINMNwbDjc7u4+d5dc5OdoSfp0vdcrzbuwGVSxbsJPqFfFn1rczuq+ro\nTOUzMY5bVLVZRtfZVLIkMBd4VlVTV4x+FfBS1XRfwsrYvgJ4LyUxrYiUB07jmHG+C1RU1Ss2Wa3s\n8Y8DeHh4NE9ISLga1W9Y1G4n6PP7OXXmPLvbfcqIrvWuyH+abLPzwLcb2HnsPPOeaU/t8j75pK3B\nUDiw2ZUf1hxi3L/7EYGXb6/D4HYBuLpI1sL5QGE2jilYdoRUK5nOyWViHONwTEsBBKhhXYvjOdoo\nmwq+CcSp6thUbVWABaraIJ3+7jj2JP9Jm5g2VZ8AYH568qkxM8erY/3Pb6NJF2k75AOQK//zvr9w\nDxNXHOSz+5rQu6lZ9TYYnOXomThe/30XK/ZH0ti/JF8PaFYgZ5GF2TiKSAPgJyDFCeI0MEhVdzsl\nn4lxTH/zyEJVD2ehmC+QpKrnRKQI8C/wIbBPVUOsPs8CN6nqPWlkBZgCnFHVYWnuVVTVE9b5C0Br\nVb0/M12McfyP42H7ODH7Zao89CO+ZctwYOd6zofvwV6nO+VKFKOEWzKRJ8KoWTfzv33+3X2Sx3/a\nzINtqvC/3g0z7WswGK5EVflj+3FenbOTm+uUY/yAq1qYy1UKuXFcC4xS1WXWdWdgjKo6VS09ww2i\nFONnLYnWspr3q+p5J3WrCEyx9h1dgFmqOl9EfhOROjhCOQ4DQ63nXMrYDrQHBgI7rarP8F/Ixkci\n0gTHsmoY8IST+hiANUv+4N7YlZzxcDjFRKybTotjP1N7RVkUF952+5E73TZx9vktlCqVvuv54ahY\nXpq9nUZ+JXjjLhOSYTBcDSJCryaVCTkVw/jloew/FW22JnKWoimGEUBVl4uI04Y+s5mjJzAR6A0c\nwrGcWhXH3uFQVS00Pslm5ujgfFwSbd5fwt0NSzOmn6P+6PkzkZw+foij7gFERCfgHbaIKmVL0Khz\n33THiE+y0efrtRw7d5H5z3ZIN0uOwWBwnrOxibT/cCm31CvPl/2b5rc6l1HIZ45zgS04llYBHgSa\nq+rdzshn5lr4Oo7wC38rnAIR8QHGA29Yh6EQ8euGfVxMsjGgw38xsCVK+1KitC81UhpaZJ5AYvSf\nuwk+cYHvh7QwhtFgyAFKFfVgUNsAJq48wPO31KJmuWL5rdL1wsPAaGCOdb3KanOKzJIA3A08lmIY\n4VLM4VPWPUMhwpacTNeVfRhX9k/qVypxVWNM33CEGRuP8lTnGnSpWz6HNTQYblwe61gNLzdXxi8L\nzbqzwSlU9ayqPqeqzazj+azC/lKTmXG0q2pcOg+MIZ3AfUPekt0g4sSEixzx60HN5rdc1fM2HIzi\nzXm7uKm2Ly/d7lT2JYPB4CRlinkysG1V5m07xqHTZgsoJxCR2iIySUT+FZGlKYfT8pnsOW4HOuPY\na0zLMistXKHgettznLTyADM2HmXe020pXsQz158XfjaOnl+toaS3O3Ofak+JIu65/kyD4UYjMjqB\nDh8upUfjSoy9t2B8vRbyPcftwDc4EtFcKiirqpudkc9s5ljCGjS9w7hU5SO/Bh3l8XOfETzZOUfd\nwyE72bjiL2w2e7afFZeYzKNTgkiy2fluUAtjGA2GXMLXx5MBrasyd+sxDkcVjj/mReR7EYmwMqql\ntDURkfUisk1EgkSkVTpy/iKyTESCRWS3iDyf6l5pEVkkIiHWZ6lU914VkVAR2edEzu9kVZ2gqhtV\ndXPK4ey7ZWgcVTVAVaurarV0DpM5Op84djCYF87+jyoeF4ijKHYnDN7xheNouHQI0eeczrkLOCpt\nvDRrO/tPRfPVA82o7mscBQyG3OSJm6rj6iJ8vexAfqviLD8CXdO0fQSMtopLvGldpyWz4hKvAEtU\ntRawxLrGun8/UN965tdWqOBlWMa1NPCniDwlIhVT2qx2p8jQW9V6aJGUlDtWRQ0P6/bW1I46hrzj\n7LEQmrseIOnBv6hcPesYw7OxiTwZ0Yunat/M42V8s/WsL5eGsnDXSUZ1q8dNtbMnazAYsk/54l70\nb+nPtA1HeKZLzQLvEa6qK61MZZc1A8Wt8xLAcdJ2cCRyOWGdR4tISnGJYBwlDTtbXacAy4GRVvsv\nqpoAHBKRUKAVsC7N8JstHVK2BIen0c2pyV1moRwf4qi3mGL1ZwC7AC8csSMjnXmAIWdp0LEX2r4H\n4uKY9IduX8PZA0G07PN8uv1/2XSUc8ke3HR7n2w95+9dJ/h08X76NKvMox2rXbPeBoPBOYZ2rsGM\njUeZsOIAY+4ulNmnhgH/iMhYHKuTmWaksYxrU2CD1VQ+JQsacBJHXV9wGM/1qUTDSadak6rmyBdW\nZnuOtwCpc5qeU9UewO04MtgY8pjkZBt2u14yjADnlnxCpe1fEHrsysImyUmJ1Fn5NA/7HaNOBee3\nifecuMCLs7bTxL8kY+5ueEXCcYPBkHtULFGEfi39mB10lOPnLua3Om7WvmHK8bgTMk8CL6iqP46y\nh5Mz6mglBf8NGJamnCHgSOLNVUZHiMi9Vmw+IvK6iMwREaezLGRmHF1UNTnV9chUyprNp3xg24Jv\nOfROQ44f/W8/otrAr7jf5WNe+SMEu/3y36GTh/dTVw/So7bzSzNRMQk8OiUIHy83Jg5sjpf7FUv6\nBoMhl3myc00AvlmR73uPyaraItUxyQmZwfwXeD8bx9LnFVjFJX4DpqVUXbI4JSIVrT4VcaxgAhwD\n/FP187PaMuINa8m2A3ArDiP9jRP6A5kbR48Uqwugqv9aypbAsbRqyGM2nrRzRCpRoVLApbYyvhV5\nrntLgg6f4c9lKy/r71ezAeVHBdP4lv5Oja+qvDBrO5ExCUwa2ILyxc0/s8GQH1QuWYR7mvvxy8aj\nnDwfn9/qZJfjwE3WeRcgJG0Hq7jEZGBPOlWX/sBhYLE+56Vqv19EPEWkGo6c3xsz0SMlfKM7MElV\n/+I/v5ksycw4fgvMtMpKAZcqdcwAvnP2AYacITHZztfHavBvw7G4uF4+m7unuR+f+/5Bl5X3EXH8\nCABnoiKJT0zC1c3tiv4Z8fu2Y6zcH8lrd9alsX/JHH8Hg8HgPE91rolNtSDMHjNERGbgcIipIyLh\nIvII8BgwzoozHINVV1dEKonIAks0pbhEFyvkY5uIdLPufQDcJiIhOGZ8HwBYpaZm4XDa+Rt4WlUv\nxS+mwzERmQjcByyw8oVnZvMuI7OqHJ9YNR1Xp8pkHgN8oKoTnH2AIWfYvDeUxISL3FrvyrRtIkKL\n3s+xaW012vtWBODAT89S8vweqo3ajJtbZn5XDs7GJvLu/D008S/JwLYBOa2+wWDIJv6lvenTtDIz\nNh7h8U7VqVQA6z2qakbLUs3T6Xsc6Gadryb9BDOoahQOn5f07r0HvOekev1whHyMtUonVuRyz9VM\nydSKquo3qloFCAACVLWqMYz5g8vy91nj+Rztq6c/o6tcowFdBr6Kp7s7p6PjmXK6Hvsr9XTKMAK8\nt2APFy4m8X6fhgW2KrnBcKPx/K21UGDsP/vyW5XCSH9VnZNSP9jygO3irLBTU0xVjTZxjflLsab3\ncDDwabw8M08Xt3vNXxz5uD0rkgOp02uEU2OvDT3Nr5vDeaxTdepVLJ61gMFgyBP8SnnzSIdqzNl6\njB3h5/JbncJGXxEZkHIhIuMBpwO2nV5/NeQv9dt3p/V9WYeWuhcpRhESaVYmgZrlsg7fiE+yMer3\nXVQt483zt9TKsr/BYMhbnupcg7LFPHh3fnC2Cw7c4PQFhohIfxGZgsPz9hFnhXPNOIqIl4hsFJHt\nVu680Vb7uyKyw9qA/VdEKmUg39XKnxcqIq+kas8w7971yo71i9m7Z1fWHYHazW4i6sElvPeoc0H/\n45eFcuh0LO/1bmjCNgyGAoiPlzsv3laHTWFn+XvXyfxWp8CTKk1cEeBRYAQQDYzOTvq4DKtypHpQ\net+y54GdqhqRzr0UOQGKqmqMFc+yGngeCE4J9hSR54BAVR2aRtYV2A/chiMLwiYc68fBIvIRcEZV\nP7CMZilVzXRKVdircoS+2wybiwd1Rq3PunM22H8qmm6fr6Jn40p8cl+THB3bYDDkHMk2O92/WM3F\nJBuLXuyEp1ve/CFbGKtyiMghLk8ckNqJQp3NDe7MzPERHKEbA6zjWxwJAdaIyMCMhNRBjHXpbh2a\nJgtCUdLPftAKCFXVg6qaCPyCI68e1ucU63wK0NuJdyi0nDh/kUfjnmJ3o1dzdFy7XXl1zk58vNwY\n1b1ejo5tMBhyFjdXF0Z1r8eRM3FMWRuW3+oUaKz0cTWBgekUz3C6aIYzxtENqKeqfVW1LxCIw6C1\nJov8qiLiKiLbcGQ4WKSqG6z290TkKA5j+2Y6opWBo6muU+fQyyjv3nXJ4j0RhGlFGrW+uiLFGTF9\n4xE2Hz7LqO6BlCmW+zUhDQbDtdGpti831/HlyyWhRMUk5Lc6BRpVtQNfXcsYzhhHf1U9leo6wmo7\nAyRlJqiqNqtsiR/QSkQaWO2jrLx704Bnrk71zPPuicjjKfkAk5OT0+tSKLBvmESPUkeo4ZtzKxun\nLsTz4cK9tKtRhr7NrsjbazAYCiivdatHXJKNz5dckXTGcCVLRKSvXGVyaGeM43IRmS8ig0VkMI4U\nPsutxABO+Rar6jlgGVfW/ZqGw6MoLZnl0Mso717aZ05KyQfobKxfQSMxIZ7eZ39gQPHtOZr8e/Sf\nu0mw2XnPJBU3GAoVtcr78ECrKkzbcITQCBNdlwVP4MjtmigiF0QkWkSuSG6eEc4Yx6eBH4Am1jEF\nR9qeWFW9OSMhEfEVkZLWeREczjV7RSR1vEAvYG864puAWiJSTUQ8cBS4/MO6l1HevesOD08vPEfs\np+69b+fIePFJNt5fuIcFO0/y/C21qFa2UO2zGwwGYNittfD2cOW9v/bktyoFGlX1UVUXVXVX1eLW\ntdOB3FlOqVRVRWQ1kIhjCXOjOhdsUxGYYnmeugCzVHW+iPwmInUAO3AYGAqOvHvAd6raTVWTReQZ\n4B/AFfjeyqsHjjx7s6wcfodxpAi6bvHyLoaX97UXQdl29Bwvz95OaEQM97Xw57GOTu9LGwyGAkSZ\nYp4826UmYxbsZeX+SDqZQuQZIiI9gU7W5XJVne+0rBOhHP2Aj3FUYxagIzBcVX+9Km3zgcIYypGc\nlMiWcb2RVo/Sskv2ChWnJj7JxmeLQ5i08gDli3vxQd9G3GT+MxkMhZqEZBu3fbKSIu6uLHi+Y66l\nfCyMoRwpiMgHQEsc23cA/YEgVXXK9d+ZzbhRQMuUmEYR8QUWA4XGOBZGdu7ZQ+m4Q1xIdHqJ/Aq2\nHjnLy7O3cyAylvtb+vNa93oU93LPQS0NBkN+4Onmyqt31uXJaVuYuekoD7SukrXQjUc3oInluYqV\nJRzZse4AACAASURBVGcrkGPG0SVNsH8UJu1crvPXEXem2j5hS+dbsy0bn2Tj08X7+XblQSoU92Lq\nw63M0ovBcJ3RtUEFWlQtxedL9tOnWWWT4Sp9SgJnrPMS2RF0xjj+LSL/4KjjCFZtrOw8xJA91G5n\nUfBJ2tYoQ7FszvRiEpLp9806gk9coH+rKrzWrS4+ZrZoMFx3iAgv3V6H/t+uZ9qGIzzSoVp+q1TQ\nGANsEZHlOLYEOwGvZCqRCmcccoaLSF8cxSnBUVF57lUoanCSvZsWMS32KXY1HJstOVVl+Ozt7DsV\nzbeDWnBb4HWdH8FguOFpW6MM7WuWYcLyUPq38sfbo3CGreUSdwHfA2eBMGCkqjqdnNbZklW/qeqL\n1mEMYy7j4V2CyKJ16Nghw0iZdJm08iALd53kla51jWE0GG4Q/t/efYdHVWYPHP8eIBB6rwYEli5g\nIAEUFERFARVXdBVWUYiKuOrPuva6WMGCyFqpim0FQcWyomKhQ6QFEKlqAAUCQUIJKef3x33DDiFl\nJmQyE+Z8nmee3PbeOXNnMu/ct97RpzW70g4zZf4voQ4l3ExwfwcALwL/FpFb/U2cb2tVEdlH3qPP\nCF4Pj1Iz8V9pbK0aqHkbdjFkwiL6dWjIuMGdrHO/MRFk2KTFLPstlR/u7l2s1SilubUqHJnEogvQ\nG6/b4EFVbeNP2nzvHHM6TObxCKgjpfFfyh/JzHn1dlJ27y78YB/Jew5w8zs/0qJeFUZd2tEyRmMi\nzB19WpN6IIOJc7eU6POKyEQR2SEiST7bYkVkoZuWcKmIdPU3rdv+vku7XES2uPG5EZGmInLQZ9+r\nhcT2NTAPr53MOrxeF35ljGCtTsPK1x9Nocf2KaTt2OJ3mkMZWdw49Ucys5TXhsRTuYLVORgTaTrE\nVOe8dvUZ/8MmUg8cLsmnnsyxw4KOAh5z42o/7Nb9TYuqXqGqsS79dOBDn90bc/blnuowDyvxBq9p\nD3QE2rvR2vximWOYSPxlD3dviuWNTtM5uU1nv9KoKg/NTGLV1r28cEWsDQdnTAS747xWpB3O5I0f\nNpXYc6rq9/yvq8SRzUBO6WJ1YFsAaY9wA4Zfzv96SgQa2+2q2hMYiNcFcRJ+jgcOljmGhazMTMZ+\nOIcG1aIZ2u9Mv9O9s/hXPkhM5v/ObsG51gDHmIjWpkE1LujQkEnztoR6SqvbgNFuWsJn8bPTfR7O\nBP5QVd8pSJq5ItXvRKTAL0sRuVlE3sfr+H8xXsvVfv4+uWWOYWDph8/zWupwnjqzvN/Foj/+uodH\nP17NWa3rcuu5rYIcoTGmNLjt3FYcysjite+L7e6xXM7Uf+4x3I80NwK3u2kJb+d/rUYDNZij7xq3\nA01ccesdwDsiUlD7l2jgeaCNqp6rqo+p6jf+PrlVUIWBk08fyLL9KZzVo0fhBwM796Xzj6k/0rB6\nRcZcERu0cRWNMaVLi3pV+Gunk5gyfwvXndGMetWij/eUmaoaH2Caa4CcLhMfAOMDfVIRKYdXHBqX\ns01V04F0t5woIhuBVsDSvM6hqoF1FM/F7hzDQIPGLTh92DNImcLfjkMZWdzw1lJSDx7m1aviqFGp\nfAlEaIwpLW49pyWZ2crL324MVQjbgF5u+WygKDMznwv8pKrJORvcNIhl3XJzoCUQtApWyxxDaN3S\nb5j/zF/5/ffkwg8GsrKVW99bxrLfUhlzRSztGlmPGmPM0U6uXZnL42N4Z9GvbEs9GNTnEpF3gQVA\naxFJdlMJXg88JyIr8IZwG+6ObSQinxWSNscgjm2I0xNY6bp2TANGqGpg/d4CeW3+Tc1YuoXjIABZ\n2cq45x7h0v3vUeOORVSpVrPQNP/6ZA0T523m4QvbkWDjKBpj8rE19SC9R3/LpXExPDWwQ5HPU9oH\nATgeQbtzFJFoEVksIitEZLWIPOa2jxaRn0RkpYjMEJEaeaRt7dPRc7mI/Ckit7l9j4rIVp99/YP1\nGoJh397dLP/qXV6c8T0vpHTjxwH/9StjnDB3MxPnbSahRzPLGI0xBTqpRkUGdW3MB0t/Y8uu8Lox\nKC2CWayaDpytqqcCsUBfETkNmA20V9WOwM/k0cxXVdf5dAKNAw4AvmO6vuDTETQsZwhZs+1Pbnhr\nKdeNmcbOR5vy/YzXAEjZtonYuSPYlDibs9vU46JOTQs91xdJ23n80zX0PaUBD1zQNsiRG2NOBDf3\nbkF0VFke+iiJSCghLG5ByxzVk+ZWo9xDVfVLVc102xcCMYWc6hy8URFKzai6G1bM5aPXH2HJlj2U\nq1qHzTW7U73eyQA0aNqWnwd8xIO33cL4q+MLHeot8Zc93PrecmIb12DMIGuZaozxT71q0dzdtzU/\nrN/FR8vz7IdvChDUOkfXsigRaAH8W1XvybX/E+B9VZ1awDkmAj+q6ji3/igwDNiL14T3TlXdU1Ac\nJV3nuHjsVTTevZDMG+bTuGG9Ip9n8679DHx5HtUrRjH9xu7UrlKhGKM0xpzosrKVS1+Zz2+7D/D1\nnb0Cbt1udY5BoqpZrmg0BugqIu1z9onIA0Am8HZ+6UWkPN50Ix/4bH4FaI5XVLsdeC6ftMNzOq5m\nZmbmdUixy/mh0WnEBMoN/+q4MsaUtHSGTVoMwORhXS1jNMYErGwZ4amBHdh7MIMnP1sb6nBKlRLp\nyqGqqcAc3CCzIjIUbyLKK7XgW9d+eHeNf/ic6w+X6WYDbwB5jviuqq+raryqxpcrF/yxDn5a+jXL\nnjqHXSkpRJWvQN1GTYt8rrT0TK5/cynb9x5i/DVdaGpjphpjiqhtw2pc37M5/1mazIKNKaEOp9QI\nZmvVujktUd1I6H2An0SkL3A3MEBVDxRymtzDByEiDX1WLwGOmu4kFBJ/2c0rn8yndsYfZB7ce1zn\n2pWWzuDXF7IieS8vDool7uTCW7IaY0xBbj2nJU1qVeKBGas4lJEV6nBKhWDeOTYE5ojISmAJMFtV\nZwHjgKrAbN85ufLoIFoZL0P9MNd5R4nIKnfe3nhj94VM4oatXD1hMSurnkmFWxbSIKZ5kc/1a8oB\nLntlPut37OONq+Po275h4YmMMaYQ0VFleeKS9mzatT+UI+eUKjYIwHH4aclX1JmVwGOV7uPBG4dR\n/zjGMUzaupehk5aQmZ3NhGu62B2jMabY3fbeMj5dtZ3P/u9MWtavWujx1iDHFEmdxq35tUpHHrn6\nguPKGOdv2MWg1xdSvqwwbcTpljEaY4LiwQvbUblCOe6fsYrs7BP/xuh4WOZYBAcO7Cc7K5s6DRrT\n+Z+zqNOwSZHPNWvlNoZOWkKjGtFM/0d3WtQr/NecMcYURZ0qFbi/f1uWbNnD+0t/C3U4Yc0yxwBl\nZ2Xx80sD+WHMVcc96sSU+Vu45d1lnNq4Oh/c0J2G1SsWU5TGGJO3v8XFcFrzWjz12Vp27DsU6nDC\nlmWOAXrpmw18u68RFU7qWOjoNgV5YfbPPPLxavq0rc9b13ajeqWoYozSGGPyJiI8eUkHDmVm8/DM\n1Ta0XD4scwzAN2t/Z8w3G/i14610u+KewhPkY9w363nx6/VcHh/Dy1d2JjqqbDFGaYwxBWtetwp3\n9mnFF6t/Z/R/14U6nLBkmaOfftuwivrv9+P8eqk8eUmHIt81Tpi7mWe//JmBnU7i6YEdKVfW3gJj\nTMkb3rM5g7s24eVvNzJ53uZQhxN2gj90zAni4J+7qVQmm4cviS/ynd47i35l5Kw19O/QgFGXdaSM\nDSJujAkREWHkxaewKy2dx2atoW7VaC7oaH2rc1g/xwBkZ2VRpmzRMsYPf0zmzg9W0Lt1PV69Ko7y\n5eyO0RgTeocysrhy/CJWJe9lSkJXTv9L7SP7rJ+jydeCqf/iq4kPk5WVXeSM8bNV27nrgxV0/0tt\nXr6ys2WMxpiwER1VlgnXxHNy7UoMf3Mpa7f/GeqQwoJ9SxdgwYZd7Fn3A7V2L6OoJaDf/PQH//fu\nMjo3qckbVxe9SNYYY4KlRqXyTEnoSuUK5bhm4mKS9xQ27LVHRCaKyA4RSfLZFisiC93woEtFJM/J\nIfJK67Y/KiJbXfrlItLfZ999IrJBRNaJyPlFfLl+sWLVfKgqF/97Hnv3p/Ppzd2oUrlKwM87b8Mu\nhk1eQpsGVZl6XTeqRVt3DWNM+Pr5j31c9sp86lStwPQR3alVpUKBxaoi0hNIA95U1fZu25fAC6r6\nucvY7lbVs/xJ67Y/CqSp6rO5jm+HNxFFV6AR8BXQSlWDMpK63TnmY2XSSrYl/8r1vVoUKWNM/GU3\n101ZSvM6lXkzoatljMaYsNeqflXGX9OF5D0HSZiypNDjVfV7YHfuzUA1t1wd2BZA2oJcDLynqumq\nuhnYQD5TFhYHyxzzkfXFg3wWfT8DY+sHnHb1Nm8Q8YbVo3nr2m4Bz75tjDGh0rVZLcYO6sSK31KL\neorbgNEi8hvwLHBfEc5xi4isdEWvOYNNnwT4jnmX7LYFhWWO+ahz8Ui2dn+cStGBDSi+cWcaV09Y\nTLXoKKZe1426VSsEKUJjjAmOvu0bMP3G7gDlXL1hzmO4H8lvBG5X1cZ4UwpOCPDpXwGaA7HAduC5\nANMXC+vnmI8mrWJp0io2oDRbUw8yZPwiROCta7vSqIaNlWqMKZ06NakJkKmq8QEmvQa41S1/AIwP\nJLGq/pGzLCJvALPc6lagsc+hMW5bUNidYy5pf+7huzFD2fTz6oDS7dyXzlXjF7EvPZM3E7rRvG7g\n9ZTGGHMC2Ab0cstnA+sDSSwiviMRXALktGb9GBgkIhVEpBnQElh8nLHmK2iZo4hEi8hiEVkhIqtF\n5DG3fbSI/OTKk2eISI180m8RkVU5zYF9ttcSkdkist79LdbJD+d+9yWn7ZlFZtpOv9PsPZDB1RMX\n8/veQ0we1oV2jaoVnsgYY0o5EXkXWAC0FpFkEbkWuB54TkRWAE8Cw92xjUTks0LSAoxy3/0rgd54\nRbOo6mrgP8Aa4AvgpmC1VIUgduUQb/DRyqqaJiJRwFy8W+1qwDeqmikizwCo6jGjeIvIFiBeVXfl\n2j4K2K2qT4vIvUDNvNL78rcrR3a20vu5b2lSMZ23bu7r1+vcn57JkAmLSNr6JxOGxnNmy7p+pTPG\nmHBnI+QEgXrS3GqUe6iqfqmqmW77Qrxy40BcDExxy1OAvx53sM43a7byS8oBrujZ0a/j0zOzuOGt\nRJb/lsrYwbGWMRpjzAkiqHWOIlJWRJYDO4DZqroo1yEJwOf5JFfgKxFJzNVCqr6qbnfLvwOB97XI\nR/2Pr+T5SlPoe0qDQo9VVW57bzlzN+xi1GWn0re9DdhrjDEniqC2VnXlwbGuXnGGiLRX1SQAEXkA\nyATezif5Gaq6VUTqAbNF5CfXadT3/CoieZYLuwx1OED58oX3M8zOyiKtfjwnVWvo1zRS03/cyudJ\nv3NfvzZcFhfoza8xxphwVmLDx4nIw8ABVX1WRIYCNwDnqGqhg/j5DickIuuAs1R1u2vV9K2qti4o\nfXHNypEjJS2dc5//juZ1q/DBDafb1FPGnCAyMjJITk7m0KFDoQ6lREVHRxMTE0NU1NEjeUVynWPQ\n7hxFpC6QoaqpIlIR6AM8IyJ9gbuBXvlljCJSGSijqvvc8nnAv9zuj/H60Tzt/n50vLGm7NjK+jXL\n6XJmP8r6cdf4xKdrSUvP5KmBHSxjNOYEkpycTNWqVWnatGmRJzQvbVSVlJQUkpOTadasWajDCRvB\nrHNsCMxxzXGX4NU5zgLGAVXxikqXi8ircEwz3/rAXNcUeDHwqap+4fY9DfQRkfXAuW79uKybNZbT\nvv07235ZV+ixP6zfyYfLtnJjr7/Qqn7V431qY0wYOXToELVr146YjBG8SY9r164dcXfLhQnanaOq\nrgQ65bG9RT7HbwP6u+VNwKn5HJcCnFNccR7KyOLurWfwt3ox3Nq8bYHHHjycxQMzkmhepzL/6J3n\nyzDGlHKRlDHmiMTXXJiIHyHnkxXbSN5fhvjzBhd67Itfr+fX3Qd44pIONi+jMcacwCI6c9TsbKp9\neRuDa2+g+19qF3js2u1/8sYPm7g8PobTCznWGGOCZcuWLbRv377wA81xieiBx3ds20z7jFXUan1G\ngcUKWdnKvR+uokbFKO7vX3DRqzHGmNIvojPH+jF/QR/6iQbZ2QUe99aCLaz4LZUXB8Xa3IzGmKAa\nOXIkU6dOpW7dujRu3Ji4uDh69+5NQkICAOedd96RYydPnsyMGTPYu3cvW7du5aqrruKRRx4JVegn\nlIjNHDU7m8xsJapcWcqWyb90eVvqQUb/dx09W9VlwKmNSjBCY0yoXfHagmO2XdixIUNOb8rBw1kM\nnXTspBCXxcXwt/jG7N5/mBunJh617/0bTi/w+ZYsWcL06dNZsWIFGRkZdO7cmbi4OIYNG8a4cePo\n2bMn//znP49Ks3jxYpKSkqhUqRJdunThggsuID4+0FmmTG4RW+e4LvEbfn+8LWuXzc33GFXl4Y9W\nk6XKE39tby26jDFBNW/ePC6++GKio6OpWrUqF110EQCpqan07NkTgCFDhhyVpk+fPtSuXZuKFSsy\ncOBA5s7N/zvN+C9i7xwXbNxNs+xGdG6Wfx3i50m/89XaP7ivXxsa16pUgtEZY8JBQXd6FcuXLXB/\nrcrlC71TLA65f7Tbj/jiEZF3jqrK+C11eKv5aKrVyLvl6YYd+7h72ko6nFSdhDNs1AhjTPD16NGD\nTz75hEOHDpGWlsasWbMAqFGjxpE7wrffPno46tmzZ7N7924OHjzIzJkz6dGjR4nHfSKKyDvHVes3\nsSd1D/37tMpzf+qBw1w7ZSnRUWV4bUgcUX4MKWeMMcerS5cuDBgwgI4dO1K/fn06dOhA9erVmTRp\nEgkJCYjIUQ1yALp27cqll15KcnIyV111ldU3FpOIzBwPfD2ahRU+Qlv9fMy+zKxsbnrnR7alHuS9\n4afRqEbFEERojIlUd911F48++igHDhygZ8+exMXF0blzZ1asWHHkmFGjRh1ZjomJYebMmaEI9YQW\nkbdENbtcztpTbqd61WPHRn3807XM25DCE3/tQNzJtUIQnTEmkg0fPpzY2Fg6d+7MpZdeSufOnUMd\nUr5EZKKI7BCRJJ9tsSKy0I2dvVREuvqb1m0fLSI/ichKEZnhpjxERJqKyEF33iPjcgfttZXUlFWh\n5O+UVe8t/pV7P1xFQo9mPHxRuxKIzBgTTtauXUvbtpE50Eder72wKatEpCeQBrypqu3dti+BF1T1\ncxHpD9ytqmf5k9ZtPw/4RlUzReQZAFW9R0SaArN8jw2miLtzTJwzg9WrEo/ZvmTLbh76KIkzW9bh\n/v5tQhCZMcaULm4C+t25NwPV3HJ1YFsAaVHVL1U1060uBEIym3xE1TlqdjYNvr+HXRWaQIevjmxP\n3nOAEW8lElOzEuMGd6acNcAxxpiiug34r4g8i3cD1v04zpUAvO+z3kxElgN7gQdV9YfjOHeBIioX\nWLXtTy49+CDbuz14ZNuBw5lc/2YihzOzeePqeKpXiirgDMYYE1HKuXrDnMdwP9LcCNyuqo2B24EJ\nRXliEXkAyARy+q5sB5qoaixwB/COiFTLL/3xiqg7x09XbWdXmTqc1s37IZOdrdz5nxX89PufTBza\nhRb1qoQ4QmOMCSuZqhpo35BrgFvd8gfA+ECfVESGAhcC56hrGKOq6UC6W04UkY1AK2BpoOf3R9Du\nHEUkWkQWi8gKEVktIo+57Xm2RMqVtrGIzBGRNS7trT77HhWRrT4tlvr7E49mZ9Ms8UmGNN55ZPDw\nMV+v5/Ok37mvXxt6t65XTK/cGGOKbuzYsbRt25aaNWvy9NNPAzBz5kzWrFlz5JjJkyezbVueVXn5\nKsGprrYBvdzy2cD6QBKLSF/gbmCAqh7w2V5XRMq65eZAS2BTsUSch2AWq6YDZ6vqqUAs0FdETgNm\nA+1VtSPwM3BfHmkzgTtVtR1wGnCTiPg2H31BVWPd4zN/gvkjeSP9M7+if/1UAD5avpWxX6/nb3Ex\nXH9m8yK/SGOMKU4vv/wys2fPZs+ePdx7771A8WSOwSAi7wILgNYikiwi1wLXA8+JyArgSWC4O7aR\niHxWSFqAcUBVYHauLhs9gZWuznEaMEJVj2nQU1yCVqzqboXT3GqUe6iqfulz2ELgsjzSbscrX0ZV\n94nIWuAkYE3uY/3VoElLDt+3mQ7ZWSz7dQ//nLaSrk1r8fglNqC4MSY8jBgxgk2bNtGvXz8SEhLY\nuHEjf//73/n444/57rvvePzxxxk8eDBLly7lyiuvpGLFiixYsIA1a9Zwxx13kJaWRp06dZg8eTIN\nGzYkMTExz6muiouqDs5nV1wex24D+vus55lWVVvks306ML0IYRZJUBvkiEhZl8vvAGar6qJchyQA\nnxdyjqZAJ8A37S2uWHaiiNT0JxZVpXyFaHanl+H6NxOpX60Crw6Jo0K5sn6/HmNMhJl0ASxz7UGy\nMrz1Fa7x5OED3nqS+74+tNdbX/Oxt74/xVtf577i9v1R6NO9+uqrNGrUiDlz5lCzpvfV1r17dwYM\nGMDo0aNZvnw599xzD/Hx8bz99tssX76ccuXKccsttzBt2rQjmeEDDzwAwLBhw3jppZeOGl3H+Ceo\nDXJUNQuIdfWKM0SkvaomQZ4tkY4hIlXwfincpqp/us2vACPx+tKMBJ7Dy2Rzpx2Ou50vHxXF6pGn\nkXXhWO794TDpGVm8e303alW2iYuNMaXbunXrSEpKok+fPgBkZWXRsGFDUlNTj5nq6vPPC7wXMT5K\npLWqqqaKyBygL5CUV0uk3EQkCi9jfFtVP/Q51x8+x7wBzMrnOV8HXgeoUKGCZmccZsyPGaxzLVNb\n1j926DhjjDnKsE//t1w26uj18pWOXo+ufvR65dpHr1etH5QQVZVTTjmFBQuOnpg5NTU1KM8XKYLZ\nWrWuz5h4FYE+wE/5tUTKlVbw+sasVdXnc+1r6LN6CXDUuHx5yVThH5WfY86GvTx0YTvOspapxphS\npGrVquzbty/P9datW7Nz584jmWNGRgarV6+mRo0aBU51ZQoWzDrHhsAcEVkJLMGrc5xFPi2RcrVk\n6gEMAc7Oo8vGKBFZ5c7bG6+TaYGkfGWSUw/y925NGNq9abG+SGOMCbZBgwYxevRoOnXqxMaNGxk6\ndCgjRowgNjaWrKwspk2bxj333MOpp55KbGws8+fPB2DSpEncdNNNxMbGEgnjaBeniBh4vEKDFjrg\nkcm8M7y7zc1ojMmXDTwe2MDjJ7LIyCmyM3njmq6WMRpjjPFLROQWZQ/uOTIqjjHGGFOYiMgcJTuz\n8IOMMQYism4uEl9zYSIiczTGGH9ER0eTkpISUZmFqpKSkkJ0dHSoQwkrETUrhzHGFCQmJobk5GR2\n7twZ6lBKVHR0NDExIZlTOGxFRGvVypUr6/79+0MdhjHGlCrWWtUYY4wxR1jmaIwxxuRimaMxxhiT\nS0TUOYpINnAw1HHkoRzezCThxuIKjMUVGIsrMKGMq6KqRuRNVKS0Vv1RVeNDHURuIrLU4vKfxRUY\niyswFpfxFZG/CIwxxpiCWOZojDHG5BIpmeProQ4gHxZXYCyuwFhcgbG4zBER0SDHGGOMCUSk3Dka\nY4wxfrPM0RhT7EREQh1DaWLXK/ycUJmjfcACY9crMHa9AhIV6gDyIiLVfZbD6f0My+sVyUp95igi\n7UXkfBEpp2FUgSoisSJyvYg0CHUsvux6BcauV2BEJF5EPgBGi8gZIlI21DEBiEg3EfkIGC8iCSJS\nIRzez3C9XqYUN8gRkZrAE0B3YCOwCXhVVTeGOK4oYBwQD6wF0oHXVXVRiOOy6xVYXHa9AotLgKeA\nc4GXgJOAjsB9qro5xLF1BCYCL+JdrwHAU6q6OoQxhe31Mp7SPELOncBhVY11X2RvA+FQTHIKUF1V\n4wBEZBKwK7QhAfBPID0Mr1cHwvN63UV4Xq/2hOH1UlUVkR+AZ1R1j4g0BJ4HwmFixK7ABlV9y72X\nlwO/5uwUESnpu0h3vb4lPK+XoZQVq4rIQBEZ61afVNXb3PJ5QC3gFBGpGqK4xrjVMsDlIlJdRAYC\npwHniEgnd2yJfcGKSGcRaeNWH1PV291yqK9XZxFp5VazCJ/r1UxEKrnVJ8LoejUTkZxp2oXwuV6D\nReQxEbkYQFU/dV/0ZwILgabAEyLSp6Riyisu4BPgEhF5AlgFxABjReQeF3eJZIwi0ktEuuWsq+oX\n4XC9TD5UNewfQDvgHWAZ3pdpfZ99PYElwI3AJGA0EBOiuBq57U/j3WnsAIYAI/H+QVuVUFzNgE+B\nBcAi4Gyffb1CeL1yx9UnTK5XU+Bz4GtgOtDaZ99ZIbxeueNq57aPBN4N4fUSYIT73A8D1rm/Vd3+\nDkBvtzwUr0izTYjiGu7z2RsFXO3We7lrdnoJxFUV+BDY7a5FTbe9jPt7Siiulz0KfoTtnWPOL2AR\n6Qm8ASxU1U549Qan5xynqt+rahdVfQXvw18XaBniuO7Dqw+6VFXfAsYAm4EewY7LuQtYrqqnAzOB\n63J2qOp3obhehcSVc70uC+H1WqSq5wBzgJEi0h5AVb8N8fXKHVcrVX0IWE0JXi9f6n2Lnw48raqT\ngJuAc4CeIlJGVVep6hx3+PdATSAtRHH1EpF+6tXjtQCS3eGJeD8u0oMdF3AY+Aa4CtgG/M3Fm+3+\nrg7F9TIFC9vMEajo/q4BzlPVsSJSHu+LKRtARI6KX1XXAvWALSGKK8PFobjiQreeglfhviaIcUXD\nkS/X/TmxANWBtSLSOufAnOtWQteroLiSRKSdu17pwBUurpK8Xjn17mvcc4/Dq6P6u4jU94m9pK9X\nXnHFAQnu/TtECX6+RORqVyxYy21aC5zkWvF+hVdc2cPF4asP3vfMvhDFtRLoLSL1gP8Cj7j3cxDe\nHVtKkOOqoarpwHjgK+BnID6nWiGPYvCgXi/jv7DLHEWkj4jMBkaJyCBV3aWq+0UkWlUP4/0TGfYV\nzAAABiJJREFUXgneLy/xlBWRASLyNbAd2FXcdS+BxOV8DPQXkdGuoUIGXovHYuUT12gRudxlNHOB\nliKyDOgLlAWmish54pqKi8jFJXS9CotrioicB3wBnC8iz5bw9crEK+7qJCKnisipQBLQBO8XPEC5\nEvx8FRZXY6AV8BlB/ny5/62GIjIHuAbv8/2SiFQDfsP7odDCHf4+0Bao7dKeLyJLgf7APaq6N4Rx\ntcSr8ngN2IpXVD0ISFDVX4Ic179FpI6qHnLfEwvw7lhzftioiFQQkXNFJJEgXC9TRKEu1/V94H2g\nFwEXA52AqcD9bl+U+9vLba/r1gU4G1gM/DUM4qrnk64jcANwSQnF9Q5wl9vXGvjQ59iHgBfc8rkl\nfL0Kiuthn7hiS/h6vQv8A69O6CFgFl4GHu9ivilE16uguN4Fbg729QLKur+tgKk524CXgSl4ndYn\n4NV5Vnf7J+M1/Mr57F8YJnFNAUa65SigQQnG9ZLv591tv8TF2wKo4GLqEIzrZY/jeE9DHoB395pT\nMX0l8LLPvgQglaMznHPdl0U5iyvfuOrj1Y29CLR1+84ApuWktbgU4FoXV84PreY++24GrnXLEmZx\nXRfEz1dZ4EngGbwffBcBU3Lt3wGcilfPOA6vbx54DUkusLiOiqsM8DvQK1e6+4ENbl/bYL2f9ij6\nI6TFqiIyDK+CfKTbtAoYJCLN3HoUXgfsZ3PSqFePEI/XOdviOjauTW7/PrzuB/8nIrcCr+HVeWgQ\nigRLa1zl8N7HF9z6ZpduOF6GvgyKv6l/McT1Y3HG4xNXL7yGKjXxvrhH4hXX9haRrgCqmgU8htc/\n72u86ZTOEJFFLt23FtdRcWUDj7pHTrq/AQ/gNbLqqF5dtgk3ocqVgSp4LRZvxftnb+O2j8ErOpqH\nV0zZAa/5fwO3PwoYDjS1uPKN63OgMl4d0C14xUqnWVwFvo/13f7b8LpudImkuNxznAkM8Vl/Ga8L\ny1Ag0W0rAzQAPsj5rAM1gJMsrgLj+g/QzCfdmcGKyx7F9P6G9Mmhifv7NPC+Wy6Ld2dxhltvjNe/\nrILF5XdcU4DyFpffcU3OeR+BShEcVyW8OrCc+rMr8YZZA1gO3OKW44F3S/B9PBHieqek4rJH8TxC\nWqyqqjlDOI0BmonI+eoVj+xV1blu3wjgIJBpcfkd1368riQWl39xHcC9j6p6IILjOqCq6S4W8LoV\n5AxnNgxoKyKz8O5wg1K0ewLHtayk4jLFJNS5c84Dr9Xddz7rXYGP8JqsF3vrMovL4rK48o2pLF5x\n4OdAC7etBV4x5RkEsajS4rJHuDzCYlYO8UbVyBaRaXj9yNLxGmms1xDOgmBxWVwRGpcA5fE6rs/A\nawSUgldM+KfFVTriMscnLGblcF8QlfA6754F/EtVvwhtVBZXoCyuwIRxXCreQOZX4o1JOklVJ4Q4\nLIvLlKiwyBydf+DVF/RRb7ilcGFxBcbiCky4xpWM193geYvLL+EalymisChWhf8VMYU6jtwsrsBY\nXIEJ17iMiXRhkzkaY4wx4SLsBh43xhhjQs0yR2OMMSYXyxyNMcaYXCxzNMYYY3KxzNGYYiQiWSKy\nXERWi8gKEblTRMrkOmaMiGzN2S4iw1ya5SJyWERWueWnRWSoiOz02b9cRNqF5tUZEzmstaoxxUhE\n0lS1iluuhzdp8jxVfcRtK4M3BdV2vPkG5+RKvwWIV9Vdbn2oW7+5xF6EMcbuHI0JFlXdgTeN2c0+\nc1WeBawGXgEGhyg0Y0whLHM0JohUdRPewNT13KbBeLM0zAAuEJEoP05zRa5i1YpBCtcY41jmaEwJ\nEZHyQH9gphuQehFwvh9J31fVWJ/HwaAGaowJq7FVjTnhiEhzvDksdwAX4k1jtMqVslbCmxN0VsgC\nNMbkyTJHY4JEROoCrwLj3MwNg4HrVPVdt78ysFlEKmkJTGZsjPGfFasaU7wq5nTlwJub8UvgMTc1\nVV/g05wDVXU/MBe4qJBz5q5z7B6s4I0xHuvKYYwxxuRid47GGGNMLpY5GmOMMblY5miMMcbkYpmj\nMcYYk4tljsYYY0wuljkaY4wxuVjmaIwxxuRimaMxxhiTy/8DwCbJL3wPp88AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf7c0df9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "f,ax = plt.subplots()#２軸のグラフの準備\n",
    "ax.plot(gdpjpy,label='gdp',linestyle=\"--\")\n",
    "ax2=ax.twinx()#２軸目をax2として設定\n",
    "ax2.plot((workpop),label='workpop')#２軸目にプロット\n",
    "results.fittedvalues.plot(label='fitted',style=':',ax=ax)\n",
    "ax.set_ylabel('log GDP')#1軸目にラベルを設定\n",
    "ax2.set_ylabel('workshop')#2軸目にラベルを設定\n",
    "ax.legend(loc='lower right')\n",
    "ax2.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "期待値は点線で示さている。労働人口（workpop）はグラフでの比較が容易になるように２軸目に表示してあることに注意してほしいです。\n",
    "\n",
    "2000年以降、期待値は実現値をうまく説明できていない。労働生産性のような別の要素が影響をしているのかもしれない"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ランダムウォークの悪魔 　\n",
    "\n",
    "つぎに残差についてみてみよう。一般に複数の非定常な時系列を最小二乗法を用いて回帰する際には、関係の無い2つの変数の間の回帰係数が有意な推定値をもつと判断されてしまう可能性がある。\n",
    "\n",
    "これを、見せかけの回帰（spurious regression）とよび、単位根問題の１つとして長い研究の歴史があります。\n",
    "\n",
    "日経平均株価、国内総生産、労働人口はランダムウォークであるので、見せかけの回帰を常に頭に置いておく必要がありますね"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcf78414a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lzHufAtyQNsvdWiIfPyRpGrqgaJ7ZwOS0nfs/ipY5m+Tv8K/ASvLTU+kmIuKk\nEpO6dUlpRBxSwTx15ebpjHsftV5FUl37g0DSD7+hEXFOmWXauvsPsjVRcqXVpNiMB6X3lfdu2fIR\ngfU2n5D0TZJ99690bLIws27wEYGZWc75ZLGZWc65EJiZ5ZwLgZlZzrkQmJnlnAuBmVnOuRCYmeXc\n/wezmKig9HDNfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf78414748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "results.resid.hist(label='residual',color='seagreen')\n",
    "plt.xlabel('residual:gdp vs work population')\n",
    "plt.ylabel('frequency')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直感的には、残差は正規分布とみなせると思うが、標本数は多くないので確認のために、Jarque-Bera（JB）の正規性の検定を紹介しますね。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jarque-Bara検定 　\n",
    "\n",
    "JBでは標本数がnの残差からその歪度（S）と尖度（K）を求め、次の検定統計量を算出し\n",
    "\n",
    "$$JB = n[ \\frac{S^2}{6} +  \\frac{(K-3)^2}{24}]  $$\n",
    "\n",
    "残差の正規性を検定しますね。統計量は自由度２の$\\chi ^{2}$乗分布に従います。帰無仮説は残差の正規性であり、得られたｐ-値(両側検定)が十分に小さければ帰無仮説を棄却し、そうでなければ採択する。 　\n",
    "\n",
    "Statsmodel.OLSのsummary()の中にはJarque-Bera（JB）が含まれている。そのｐ-値は0.00297と非常に小さい。したがって残差は正規分布に従っているとはいえない\n",
    "\n",
    "\n",
    "### プログラムコードの解説：データの取得と準備\n",
    "\n",
    "workpop = pdr.DataReader('LFWA64TTJPM647S',\"fred\",start,end).dropna()\n",
    "\n",
    "労働人口の取得\n",
    "\n",
    "gdp = pdr.DataReader('MKTGDPJPA646NWDB',\"fred\",start,end).dropna()\n",
    "\n",
    "ドル建て国内総生産の取得\n",
    "\n",
    "gdp=gdp.resample('A',loffset='-1d').last().dropna()\n",
    "\n",
    "データの日付が1月1日であるので、これを12月30日に変更\n",
    "\n",
    "fx = pdr.DataReader('DEXJPUS',\"fred\",start,end).dropna()\n",
    "\n",
    "ドル／円データの取得\n",
    "\n",
    "fx=fx.resample('A',loffset='-1d').last().dropna()\n",
    "\n",
    "ドル／円のデータを年次に変換し、12月30日に一番近い日のデータを用いる。\n",
    "\n",
    "workpop=workpop['1972':].resample('A',loffset='-1d').last().dropna()\n",
    "\n",
    "労働人口のデータを年次に変換し12月30日に近いデータを使うためにlast()を用した。\n",
    "\n",
    "'A'は年次にデータを変換するか日付は年初になってしまう。それでoffset='-ld'を用いて\n",
    "\n",
    "日付を1日前にずらしている。\n",
    "\n",
    "\n",
    "x=sm.add_constant(workpop) 切片のために列の追加\n",
    "\n",
    "model=sm.OLS(gdpjpy,x)     回帰モデルの設定\n",
    "\n",
    "results=model.fit()        モデルの最適化と結果の格納\n",
    "\n",
    "print(results.summary())   結果の要約を出力\n",
    "\n",
    "## 多変量解析 　\n",
    " つぎに日経平均株価を被説明変数、労働人口と国内総生産を説明変数として多回帰分析を試みてみよう。日経平均株価も対数をとってあることを忘れないでほしいです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   n225   R-squared:                       0.552\n",
      "Model:                            OLS   Adj. R-squared:                  0.542\n",
      "Method:                 Least Squares   F-statistic:                     53.09\n",
      "Date:                Fri, 04 Aug 2017   Prob (F-statistic):           4.97e-09\n",
      "Time:                        02:32:52   Log-Likelihood:                -18.712\n",
      "No. Observations:                  45   AIC:                             41.42\n",
      "Df Residuals:                      43   BIC:                             45.04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -19.4310      3.956     -4.912      0.000     -27.409     -11.453\n",
      "workpop        0.4296      0.059      7.286      0.000       0.311       0.549\n",
      "gdpjpy         0.4296      0.059      7.286      0.000       0.311       0.549\n",
      "==============================================================================\n",
      "Omnibus:                        6.742   Durbin-Watson:                   0.335\n",
      "Prob(Omnibus):                  0.034   Jarque-Bera (JB):                5.702\n",
      "Skew:                           0.837   Prob(JB):                       0.0578\n",
      "Kurtosis:                       3.489   Cond. No.                     7.57e+17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.77e-31. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "lnn225 = np.log(pdr.DataReader(\"NIKKEI225\", 'fred',start,end).dropna())\n",
    "lnn225=lnn225.resample('A',loffset='-1d').last().dropna()\n",
    "port=pd.concat([lnn225,x,gdpjpy],axis=1).dropna()\n",
    "port.columns=[\"n225\",\"const\",\"workpop\",\"gdpjpy\"]\n",
    "model=sm.OLS(port.n225,port.iloc[0:,1:])\n",
    "results=model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$R^{2}$は0.552です。また、それぞれの回帰係数のp-値もどれも１％以下です。\n",
    "\n",
    "また、JBのp-値は１％以下。結果を目で見で確認してみます。\n",
    "\n",
    "日経平均株価の対数とその期待値をプロットして、モデルの期待値がどれほど実際の指数の動きを説明できるかみてみよう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa85b4e22e8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E9onI/wH/p5R6EXgM3Xe0GEopL+Am4MVCmz8H3kRrgm8CHwATHHVRhTEEnYHrE7kUPP0g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa85b602e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#多変量解析：折れ線グラフ\n",
    "f,ax = plt.subplots()#２軸のグラフの準備\n",
    "(port.gdpjpy-24).plot(label='gdp',linestyle=\"--\",ax=ax)\n",
    "port.n225.plot(label='n225',ax=ax)\n",
    "ax2=ax.twinx()#２軸目をax2として設定\n",
    "(port.workpop).plot(label='workpop',ax=ax2,style='o')\n",
    "results.fittedvalues.plot(label='fitted',style=':',ax=ax)\n",
    "plt.legend(loc='upper left')\n",
    "ax.set_ylabel('log Nikkei225 index')#1軸目にラベルを設定\n",
    "ax2.set_ylabel('workshop')#2軸目にラベルを設定\n",
    "ax.legend(loc='lower right')\n",
    "ax2.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "バブル成長期に日経平均株価がオーバーシュートし、バブル崩壊により期待値に接近している。\n",
    "\n",
    "また、バブル崩壊後には米国のインターネットバブル崩壊、日本の第３次平成不況期に期待値を下方にオーバーシュートし、その後回復している。\n",
    "\n",
    "また、リーマンショックで期待値を下回る様子が見てとれる。 　\n",
    "\n",
    "JBは0.203で正規分布ではないとはいいきれない状態にある。一応、目視で残差を確認しておきます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa860cd2a90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa860cd25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#多変量解析：ヒストグラム\n",
    "results.resid.hist(label='residual',color='mistyrose')\n",
    "plt.xlabel('residual:gdp vs work population')\n",
    "plt.ylabel('frequency')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 経済の構造変化\n",
    "\n",
    "日経平均株価、国内総生産、労働人口がバブルの崩壊を機にそれ以前の上昇から、下落のトレンドに転換したようにみえるので、それを確かめてみよう。分析はバブル崩壊前と後に２つ時期に分けて行った"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa85b292f60>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8hF9G/ULtarXLzc7isu7gae6buY7jSRk8dnlrOjX0zI+CSsJuYJWIfI+1ZzYC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\n2QAAABJJREFUhmEYIY8ZM8MwDCPk+f9oB78prGT5wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa85b416be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#バブル崩壊前のグラフ\n",
    "f,ax = plt.subplots()#２軸のグラフの準備\n",
    "(port[:'1990/1/1'].gdpjpy-24).plot(label='gdp',linestyle=\"--\",ax=ax)\n",
    "port[:'1990/1/1'].n225.plot(label='n225',ax=ax)\n",
    "ax2=ax.twinx()#２軸目をax2として設定\n",
    "(port[:'1990/1/1'].workpop).plot(label='workpop',style='o',ax=ax2)\n",
    "#書籍のグラフはworkpopから8.5引いてしまっているので、こちらが正しいグラフです。\n",
    "results_b.fittedvalues.plot(label='fitted',style=':',ax=ax)\n",
    "ax.set_ylabel('log Nikkei225 index')#1軸目にラベルを設定\n",
    "ax2.set_ylabel('workshop')#2軸目にラベルを設定\n",
    "ax.legend(loc='lower right')\n",
    "ax2.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JBで0.612となった残差の状態をグラフで確認しよう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcf782f03c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wfgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf782f07b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#バブル崩壊前：ヒストグラム\n",
    "results_b.resid.hist(label='residual',color='seagreen')\n",
    "plt.xlabel('residual:gdp vs work population')\n",
    "plt.ylabel('frequency')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## バブル崩壊後 　\n",
    "\n",
    "つぎにバブル崩壊後について崩壊前と同じ分析を行ってみよう。結果はつぎのとおりです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   n225   R-squared:                       0.019\n",
      "Model:                            OLS   Adj. R-squared:                 -0.020\n",
      "Method:                 Least Squares   F-statistic:                    0.4820\n",
      "Date:                Fri, 04 Aug 2017   Prob (F-statistic):              0.494\n",
      "Time:                        02:02:57   Log-Likelihood:                -5.9628\n",
      "No. Observations:                  27   AIC:                             15.93\n",
      "Df Residuals:                      25   BIC:                             18.52\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -6.3701     22.986     -0.277      0.784     -53.711      40.971\n",
      "workpop        0.2356      0.339      0.694      0.494      -0.463       0.935\n",
      "gdpjpy         0.2356      0.339      0.694      0.494      -0.463       0.935\n",
      "==============================================================================\n",
      "Omnibus:                        1.826   Durbin-Watson:                   0.511\n",
      "Prob(Omnibus):                  0.401   Jarque-Bera (JB):                1.108\n",
      "Skew:                          -0.132   Prob(JB):                        0.575\n",
      "Kurtosis:                       2.044   Cond. No.                     1.22e+19\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 4.17e-34. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "#バブル崩壊後\n",
    "port_a=port['1990/1/1':]\n",
    "results_a=(sm.OLS(port_a.n225,port_a.iloc[0:,1:])).fit()\n",
    "print(results_a.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$R^{2}$、AIC、BICともに悪化している。回帰係数のｐ-値もすべて20％以上である。これはどの説明変数も被説明変数の動きをとらえていない可能性を示唆しているのである。\n",
    "\n",
    "このように経済はある時点を境に大きく変化する可能性がある。 　\n",
    "\n",
    "そこで説明変数としてドル円の為替レートを加えてみよう。特にバブル崩壊後は強い円高に日本経済は苦しめられてきた"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   n225   R-squared:                       0.555\n",
      "Model:                            OLS   Adj. R-squared:                  0.534\n",
      "Method:                 Least Squares   F-statistic:                     26.22\n",
      "Date:                Fri, 04 Aug 2017   Prob (F-statistic):           4.07e-08\n",
      "Time:                        02:11:48   Log-Likelihood:                -18.571\n",
      "No. Observations:                  45   AIC:                             43.14\n",
      "Df Residuals:                      42   BIC:                             48.56\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -24.0115      9.763     -2.459      0.018     -43.715      -4.308\n",
      "workpop        0.4865      0.126      3.874      0.000       0.233       0.740\n",
      "gdpjpy         0.4865      0.126      3.874      0.000       0.233       0.740\n",
      "fx             0.1545      0.300      0.514      0.610      -0.452       0.761\n",
      "==============================================================================\n",
      "Omnibus:                        7.804   Durbin-Watson:                   0.358\n",
      "Prob(Omnibus):                  0.020   Jarque-Bera (JB):                6.884\n",
      "Skew:                           0.921   Prob(JB):                       0.0320\n",
      "Kurtosis:                       3.530   Cond. No.                     7.72e+17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.72e-31. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "lnn225 = np.log(pdr.DataReader(\"NIKKEI225\", 'fred',start,end).dropna())\n",
    "lnn225=lnn225.resample('A',loffset='-1d').last().dropna()\n",
    "lnfx=np.log(fx)\n",
    "port1=pd.concat([lnn225,x,gdpjpy,lnfx],axis=1).dropna()\n",
    "port1.columns=[\"n225\",\"const\",\"workpop\",\"gdpjpy\",\"fx\"]\n",
    "model1=sm.OLS(port1.n225,port1.iloc[0:,1:])\n",
    "results1=model1.fit()\n",
    "print(results1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   n225   R-squared:                       0.324\n",
      "Model:                            OLS   Adj. R-squared:                  0.268\n",
      "Method:                 Least Squares   F-statistic:                     5.753\n",
      "Date:                Fri, 04 Aug 2017   Prob (F-statistic):            0.00910\n",
      "Time:                        02:12:06   Log-Likelihood:               -0.93333\n",
      "No. Observations:                  27   AIC:                             7.867\n",
      "Df Residuals:                      24   BIC:                             11.75\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.7868     19.766      0.242      0.811     -36.008      45.581\n",
      "workpop       -0.0144      0.297     -0.048      0.962      -0.628       0.599\n",
      "gdpjpy        -0.0144      0.297     -0.048      0.962      -0.628       0.599\n",
      "fx             1.2308      0.374      3.292      0.003       0.459       2.002\n",
      "==============================================================================\n",
      "Omnibus:                        2.948   Durbin-Watson:                   0.792\n",
      "Prob(Omnibus):                  0.229   Jarque-Bera (JB):                1.802\n",
      "Skew:                          -0.619   Prob(JB):                        0.406\n",
      "Kurtosis:                       3.260   Cond. No.                     1.10e+19\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 5.2e-34. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "#バブル崩壊後：要素にドル円の為替レートを追加\n",
    "port1_a=port1['1990/1/1':]\n",
    "results1_a=(sm.OLS(port1_a.n225,port1_a.iloc[0:,1:])).fit()\n",
    "print(results1_a.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ドル円の為替レートを加えたことで大きな変化が現れた。それはそれぞれの回帰係数のｐ-値である。\n",
    "\n",
    "切片、国内総生産、そして労働人口の回帰係数が大幅に上昇し、一方でドル円の為替レートの回帰係数は５％以下に収まっている。\n",
    "\n",
    "また、$R^{2}$、AIC、BICともに前のモデルよりかは改善している。しかしJBに関しては若干の悪化がみられる。\n",
    "\n",
    "モデルが示している期待値と実際の日経株価平均、ドル円の為替レートをプロットしてみよう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe2f1e272b0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yE+WOzdfmS7lmipSzGkhpIKS4NCk5KTInP6fMcZ1OJyf+cVg2nbFWHr6cWHZi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2f2af5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##バブル崩壊後のグラフ：ドル円の為替レート\n",
    "(port1['1990/1/1':].fx+5).plot(label='fx',linestyle=\"--\")\n",
    "port1['1990/1/1':].n225.plot(label='n225')\n",
    "results1_a.fittedvalues.plot(label='fitted',style=':')\n",
    "plt.ylabel('log Nikkei225 Index')\n",
    "plt.legend(loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "グラフからはリーマンショック以降に期待値は実際の動きをよく説明している。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 経済は生き物 　\n",
    "バブル崩壊後の経済をもう少し細かくみてみよう。これらは過去を振り返った分析であるので注意が必要である。\n",
    "\n",
    "期間は1990/1/1から2000/1/1、2000/1/1から2008/1/1、2008/1/1以降の３つに分割した。 \n",
    "\n",
    "結果をつぎの表にまとめました。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared:  0.637234115352  F-pvalue:  0.0889983480357  AIC:  -10.4131864571  BIC:  -9.20284608516\n",
      "pvalues: \n",
      "const      0.023541\n",
      "gdpjpy     0.254732\n",
      "workpop    0.026042\n",
      "fx         0.095029\n",
      "dtype: float64\n",
      "jbpv:  0.651686690925\n"
     ]
    }
   ],
   "source": [
    "#バブル崩壊後：細分化\n",
    "def report(port):\n",
    "    results1_a=(sm.OLS(port1_a.n225,port1_a.ix[0:,['const','gdpjpy','workpop','fx']]))\\\n",
    "    .fit()\n",
    "    print(\"R-squared: \",results1_a.rsquared,\" F-pvalue: \",results1_a.f_pvalue,\" AIC: \"\\\n",
    "          ,results1_a.aic,\" BIC: \",results1_a.bic)\n",
    "    print(\"pvalues: \")\n",
    "    print(results1_a.pvalues)\n",
    "    from statsmodels.compat import lzip\n",
    "    import statsmodels.stats.api as sms\n",
    "    test=sms.jarque_bera(results1_a.resid)\n",
    "    print(\"jbpv: \",test[1])\n",
    "port1_a=port1['1990/1/1':'2000/1/1']\n",
    "report(port1_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared:  0.926156045386  F-pvalue:  0.00996898531012  AIC:  -13.7258843227  BIC:  -13.408118156\n",
      "pvalues: \n",
      "const      0.061178\n",
      "gdpjpy     0.006358\n",
      "workpop    0.007690\n",
      "fx         0.057891\n",
      "dtype: float64\n",
      "jbpv:  0.731005909439\n"
     ]
    }
   ],
   "source": [
    "#バブル崩壊後：細分化2\n",
    "port1_a=port1['2000/1/1':'2008/1/1']\n",
    "report(port1_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared:  0.949635785933  F-pvalue:  0.00467542454257  AIC:  -12.4292010344  BIC:  -12.1114348677\n",
      "pvalues: \n",
      "const      0.182073\n",
      "gdpjpy     0.726831\n",
      "workpop    0.122300\n",
      "fx         0.050233\n",
      "dtype: float64\n",
      "jbpv:  0.600603318664\n"
     ]
    }
   ],
   "source": [
    "#バブル崩壊後：細分化3\n",
    "port1_a=port1['2008/1/1':]\n",
    "report(port1_a)"
   ]
  },
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   "cell_type": "markdown",
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    "\n",
    "|                   | 1990以前     |1990以降      |1990ー2000   |2000ー2008   |2008以降\n",
    "|-------------------|-------------|-------------|-------------|-------------|-----------|\n",
    "| R2            |0.94       |0.32      |0.69      |0.93            |0.95     |\n",
    "|Fp-値               |0.00       |0.03      |0.05      |0.01           |0.00     |\n",
    "|AIC            |-7.45      |10.18     |-12.18    |-13.76       |-12.37   |\n",
    "|BIC                |-4.78         |15.22        |-10.92      | -13.45     | -12.05     | \n",
    "|回帰係数 p-値         |              |             |            |            |            |\n",
    "|切片                  |0.00          |0.99         |0.01        |0.05        |0.17        |\n",
    "|国内総生産(gdp)      |0.02          |0.99         |0.13        |0.01        |0.76        |\n",
    "|労働人口(workpop)    |0.00         |0.87         |0.01        |0.01        |0.11         |    \n",
    "|ドル／円(fx)         |-            |0.01         |0.06        |0.06        |0.05         |\n",
    "|JB p-値               |0.61         |0.47         |0.84        |0.73        |0.60         |\n",
    "\n",
    "                                                             ※fitted=期待値 n225=日経平均株価\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表からわかるようにそれぞれの期間で説明変数のp-値は大きく異なり、被説明変数に与える影響が異なるという結果になっている。\n",
    "\n",
    "1990－2000の期間では労働人口とドル円の為替レートが日経平均株価の有力な説明変数になっている一方、2000－2008の期間ではそれに国内総生産が加わる。\n",
    "\n",
    "2008以降ではドル円の為替レートだけが有力な説明変数である。これは期間の取り方により微妙に変化する\n",
    "\n",
    "\n",
    "### プログラムコードの解説：report関数\n",
    "   \n",
    "   print(\"R-squared: \",results1_a.rsquared,\" F-pvalue: \",results1_a.f_pvalue,\" AIC: \"\\\n",
    "          ,results1_a.aic,\" BIC: \",results1_a.bic)\n",
    "   \n",
    "   演算結果の受け渡し\n",
    "   \n",
    "   R-squared: self.rsqaured\n",
    "  \n",
    "  F-p-値: self.f_pvalue\n",
    "  \n",
    "  AIC: self.aic\n",
    "  \n",
    "  BIC: self.bic\n",
    "    \n",
    "    print(\"pvalues: \")\n",
    "  \n",
    "  print(results1_a.pvalues)\n",
    "   \n",
    "   回帰係数の出力：self.pvalues\n",
    "    \n",
    "   \n",
    "   from statsmodels.compat import lzip\n",
    "  \n",
    "  import statsmodels.stats.api as sms\n",
    " \n",
    " test=sms.jarque_bera(results1_a.resid)\n",
    " \n",
    " JBに関してはOLS.fit()で演算されているにもかかわらず、\n",
    " \n",
    " 取り出すことができないので別途計算した。演算結果をtestに格納    \n",
    "\n",
    "\n",
    " \n",
    " print(\"jbpv: \",test[1])\n",
    " \n",
    " [0]は統計量、[1]はp-値である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多変量解析の原理 　\n",
    " \n",
    " ここで用いた多変量解析のモデルは、実は自己回帰移動平均（ARMA）モデルで使用したものと同じ原理です。\n",
    " \n",
    " Material８での編自己相関を思い出してほしい。\n",
    "\n",
    "自己回帰モデルAR（n）とは実現値となる被説明変数がn個の自己の過去の変数の和のモデルであった。\n",
    " \n",
    " このように複数の要因(確率変数)があり、２つの特定の組となる変数の間の相関を求めたいときに、他の確率変数の動きを固定して、編自己相関なるものを\n",
    " \n",
    "求めた。これは条件付確率とよばれる方法を用いている。同じ方法が多変量解析にも用いられているのです。\n",
    " \n",
    " \n",
    "## 全般を通して　\n",
    "\n",
    "長期投資の収益源である確定的トレンドの存在と短期の収益源である季節性の存在が、統計的には必ずしも有意だとは限らない事実をみてみよう。 　\n",
    "\n",
    "確定的トレンドや季節性、マクロ経済指標との関係などの判断は統計的手法、数学的モデルだけではなく、その現象の背後にある事実関係を知る必要があります。\n",
    "\n",
    "統計的にモデルの正当性が今説明できなくとも、現実を見て人々が経済の発展(回復)をもたらす強い政策と意思と努力があるのだと確信できればそのモデルは正しくなる。\n",
    "\n",
    "これらすべての問題点を認識した時点が出発点です。"
   ]
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