11b_Money_Inflation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# インフレとマネーストック"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div name=\"html-admonition\" style=\"font-size: 0.8em\">\n",
    "<input type=\"button\" onclick=\"location.href='https://translate.google.com/translate?hl=&sl=ja&tl=en&u='+window.location;\" value=\"Google translation\" style=\"color:#ffffff;background-color:#008080; height:25px\" onmouseover=\"this.style.background='#99ccff'\" onmouseout=\"this.style.background='#008080'\"/> in English or the language of your choice.\n",
    "</div><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import japanize_matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import py4macro\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# numpy v1の表示を使用\n",
    "np.set_printoptions(legacy='1.21')\n",
    "# 警告メッセージを非表示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## はじめに"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "前章に続けて、経済の「真のメカニズム」の反映としてデータに現れる何らかのパターンを考察する。\n",
    "ここでは、マネーストックとインフレーションとの関係に着目しデータの特徴を考える。\n",
    "\n",
    "この２つの変数の関係を示す考え方として、貨幣数量説があり、次式で表される。\n",
    "\n",
    "$$\n",
    "P_tY_t=M_tV_t\n",
    "$$ (eq:11-qtm)\n",
    "\n",
    "* $P_t$：一般物価水準\n",
    "    * ある期間（1年間）で取引された財の集計物価水準\n",
    "* $Y_t$：実質支出（GDP）\n",
    "    * ある期間（1年間）で取引された財に対する実質支出額\n",
    "* $M_t$：マネーストック\n",
    "    * ある期間（1年間）平均で流通した貨幣量\n",
    "* $V_t$：貨幣の流通速度\n",
    "    * ある期間（1年間）平均で貨幣１単位が何回使用されたかを示す"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "式[](eq:11-qtm)に対数を取り時間微分すると次式となる。\n",
    "\n",
    "$$\n",
    "\\pi_t+g_t=m_t+v_t\n",
    "$$ (eq:11-qtm_growth)\n",
    "\n",
    "ここで\n",
    "* $\\pi_t=\\dfrac{\\dot{P}_t}{P_t}$：インフレ率\n",
    "* $g_t=\\dfrac{\\dot{Y_t}}{Y_t}$：実質GDPの成長率\n",
    "* $m_t\\equiv\\dfrac{\\dot{M}_t}{M_t}$：マネーストックの増加率\n",
    "* $v_t=\\dfrac{\\dot{V}_t}{V_t}$：貨幣の流通速度の変化率\n",
    "\n",
    "式[](eq:11-qtm)は恒等式であり，式[](eq:11-qtm_growth)も常に成り立つ関係である。ここで長期均衡を考えてみよう。GDPは供給サイドで決定され，成長率は一定（$g_t=\\overline{g}$）としよう（ソロー・モデルを考えてみよう）。更に貨幣の流通速度は一定とする。この仮定のもと式[](eq:11-qtm_growth)は次式としてまとめることができる。\n",
    "\n",
    "$$\n",
    "\\pi_t = m_t-\\overline{g}\n",
    "$$ (eq:11-qtm_growth_long)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "この式によると，**長期的**なインフレ率はマネーストックの増加率によって決定される。この節では，式[](eq:11-qtm_growth_long)の予測がデータと整合的かどうかを議論する。次の２つのデータ・セットを使いこの問題を考察する。\n",
    "* 日本の時系列データ\n",
    "* 世界経済のパネル・データ\n",
    "\n",
    "手法としては散布図と回帰直線の傾きに基づいて正の相関があるかを考える。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 日本の時系列データ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 説明"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`py4macro`には`jpn-money`というデータ・セットが含まれており，その内容は次のコードで確認できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    | `cpi`: 消費者物価指数\n",
      "    |   * 2015年の値を`100`\n",
      "    |   * 季節調整済み\n",
      "    | `money`: マネーストック（M1）\n",
      "    |   * 2015年の値を`100`\n",
      "    |   * 季節調整済み\n",
      "    |\n",
      "    | * 月次データ\n",
      "    | * 1955年1月〜2020年12月\n",
      "    |\n",
      "    | ＜出典＞\n",
      "    | OECD Main Economic Indicators\n"
     ]
    }
   ],
   "source": [
    "py4macro.data('jpn-money', description=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "1955年1月から2021年4月までの月次データであり，消費者物価指数とマネーストックの２つの変数が含まれている。マネーストックにはM1が使われており，現金通貨と要求払預金(預入期間の設定がなく自由に出し入れができる預金のことであり，普通預金が典型的な例)で構成される。詳しくは[マネーストック統計の解説](https://www.boj.or.jp/statistics/outline/exp/data/exms01.pdf)を参考にしてほしい。M1を使う大きな理由は長い時系列データが存在することであり，長期的な関係である式[](eq:11-qtm_growth_long)を考えるには適しているためである。\n",
    "\n",
    "一方で長い時系列データであれば長期的な関係を捉えることができるという訳ではない。ここでの長期的な関係とは，ある作用が発生した後，その効果が現れるのに時間が掛かるという意味である。例えば，今日マネーストックの増加率が上昇したとしても，明日すぐにインフレ率の上昇につながるという訳ではなく，その効果が浸透しデータの数字に現れるまで数ヶ月掛かる事になる。この点を示すために次のステップで進めることにする。\n",
    "1. 月次データを変換して次のデータを作成する。\n",
    "    * 四半期データ\n",
    "    * 年次データ\n",
    "    * 1期を3年とするデータ（ここでは「3年次データ」と呼ぶ）\n",
    "2. 月次データを含む４つのデータ・セットを使い，２変数の散布図と回帰分析をおこなう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### `resample()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "まず四半期データへの変換を考えよう。１四半期には３ヶ月の値があり，その平均を１四半期の値とする。同様に，年次データおよび3年次データに変換する場合は，12ヶ月間もしくは36ヶ月間の値を使い平均を計算することになる。このような計算は「連続的な時系列のグループ計算」として捉えることができる。グループ計算のメソッドに`groupby`があるが，カテゴリー変数に基づいてグループ分けするので，この問題に使うことはできない。その代わりに，時系列用グループ計算メソッドとして`resample`が用意されている。ここでは`resample`の使い方を紹介するが，異なる方法として移動平均を使うことも可能であり，興味がある人は[こちらを参照してみよう](https://py4basics.github.io/Moving_Average.html)。\n",
    "\n",
    "```{note}\n",
    "`resample`はグループ計算だけではなく，その「逆の計算」もすることができるがここでは触れない。以下では，「時系列グループ計算用」としてのみ考える。\n",
    "```\n",
    "\n",
    "`resample`の使い方を説明するために，次の`DataFrame`（変数名は`df_ex`）を考えよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "date_index = pd.date_range('2019-01-01','2020-12-01', freq='MS')\n",
    "df_ex = pd.DataFrame({'X':list( range(10, 120+1, 10) )*2,\n",
    "                      'Y':np.random.normal(5, 1, size=12*2)\n",
    "                     }, index=date_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "output_scroll"
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       "      <th>2019-01-01</th>\n",
       "      <td>10</td>\n",
       "      <td>4.767896</td>\n",
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       "      <th>2019-02-01</th>\n",
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       "      <td>5.285442</td>\n",
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       "      <th>2020-07-01</th>\n",
       "      <td>70</td>\n",
       "      <td>3.801658</td>\n",
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       "      <th>2020-08-01</th>\n",
       "      <td>80</td>\n",
       "      <td>3.973230</td>\n",
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       "      <th>2020-09-01</th>\n",
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       "              X         Y\n",
       "2019-01-01   10  4.767896\n",
       "2019-02-01   20  5.285442\n",
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       "2019-04-01   40  1.988678\n",
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       "2020-03-01   30  6.279246\n",
       "2020-04-01   40  6.402422\n",
       "2020-05-01   50  5.598811\n",
       "2020-06-01   60  6.488859\n",
       "2020-07-01   70  3.801658\n",
       "2020-08-01   80  3.973230\n",
       "2020-09-01   90  3.085481\n",
       "2020-10-01  100  5.482045\n",
       "2020-11-01  110  4.342102\n",
       "2020-12-01  120  4.935105"
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   "source": [
    "df_ex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_ex`には時系列用の行ラベルがが使われており，毎月の最初の日がラベルとなっている。列`X`には`10`から`120`までの整数が昇順に並んでおり，`Y`にはランダムは値が並んでいる。\n",
    "\n",
    "２つのステップに分けて説明する。\n",
    "\n",
    "**ステップ１**：グループ化の期間を指定する<br>\n",
    "最初のステップでは，グループ化する期間を引数として`.resample()`を実行する。指定する期間は次のように文字列として指定する。\n",
    "* `YS`：１年を基準としてグループ化（`Y`は`year`、`S`は`start of the period`を表している）\n",
    "* `QS`：四半期を基準としてグループ化（`Q`は`quarter`、`S`は`start of the period`を表している）\n",
    "* `MS`：１ヶ月を基準としてグループ化（`M`は`month`、`S`は`start of the period`を表している）\n",
    "    * 3ヶ月であれば`3MS`，１年であれば`12MS`とできる。\n",
    "\n",
    "グループ化の期間を示す引数には様々なものがあり、[このサイト](https://pandas.pydata.org/docs/user_guide/timeseries.html#offset-aliases)を参考にすると良いだろう。\n",
    "\n",
    "`df_ex`を四半期でグループ化するには次のようになる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x14a235c10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ex.resample('QS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "このコードは`DataFrame`を返すわけではない。返すのは時系列グループ計算用のオブジェクトであり，それを使ってグループ計算をおこなう事になる。\n",
    "\n",
    "**ステップ２**：計算内容を指定する。<br>\n",
    "どのような計算をしたいかを指定する。ここではメソッド.mean()を使って指定した期間内の平均を計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <th>2019-01-01</th>\n",
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       "      <td>3.620123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>110.0</td>\n",
       "      <td>4.919751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X         Y\n",
       "2019-01-01   20.0  5.190616\n",
       "2019-04-01   50.0  4.009998\n",
       "2019-07-01   80.0  5.905923\n",
       "2019-10-01  110.0  4.948072\n",
       "2020-01-01   20.0  4.909451\n",
       "2020-04-01   50.0  6.163364\n",
       "2020-07-01   80.0  3.620123\n",
       "2020-10-01  110.0  4.919751"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ex.resample('QS').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行ラベルには四半期の最初の日が使われている。列`X`に並んでいる数字から，毎四半期の期間内平均であることが確認できる。\n",
    "\n",
    "次のコードは年平均を計算している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>65.0</td>\n",
       "      <td>5.013652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>65.0</td>\n",
       "      <td>4.903172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               X         Y\n",
       "2019-01-01  65.0  5.013652\n",
       "2020-01-01  65.0  4.903172"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ex.resample('YS').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "平均以外にも様々な計算ができるようになっている。`py4macro`モジュールに含まれる`see()`関数を使って属性を調べてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".X                  .Y                  .agg                .aggregate\n",
      ".apply              .asfreq             .ax                 .bfill\n",
      ".binner             .count              .ffill              .fillna\n",
      ".first              .get_group          .groups             .include_groups\n",
      ".indices            .interpolate        .kind               .last\n",
      ".max                .mean               .median             .min\n",
      ".ndim               .nearest            .ngroups            .nunique\n",
      ".ohlc               .pipe               .prod               .quantile\n",
      ".sem                .size               .std                .sum\n",
      ".transform          .var\n"
     ]
    }
   ],
   "source": [
    "py4macro.see(df_ex.resample('YS'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主なメソッドとして次を挙げることができる（これらの計算で欠損値は無視される）。\n",
    "* `mean()`：平均\n",
    "* `median()`：中央値\n",
    "* `max()`：最大値\n",
    "* `min()`：最小値\n",
    "* `std()`：標準偏差\n",
    "* `var()`：分散\n",
    "* `sum()`：合計\n",
    "* `first()`：最初の値\n",
    "* `last()`：最後の値\n",
    "* `count()`：要素数\n",
    "\n",
    "このリストにない計算をしたい場合は，上のリストにある.agg()（aggregate()も同じ）を使い`NumPy`や自作の関数を指定することができる。例えば，変動係数（coefficient of variation）を計算したいとしよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv(x):\n",
    "    \n",
    "    return x.std() / x.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この関数の`x`は，時系列グループ計算で取り出された`Series`もしくは`DataFrame`と考えれば良いだろう。使い方は簡単で，`.agg()`の引数として`cv`を指定するだけである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.5547</td>\n",
       "      <td>0.273479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.5547</td>\n",
       "      <td>0.231164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 X         Y\n",
       "2019-01-01  0.5547  0.273479\n",
       "2020-01-01  0.5547  0.231164"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ex.resample('YS').agg(cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "`.agg()`の引数は`cv`であり`cv(x)`ではない。関数名だけを`.agg()`に渡し，`.agg()`が渡された関数を実行するというイメージである。`cv(x)`を引数に使うと，`cv(x)`を実行した**結果**を`.agg()`に渡すことになりエラーとなってしまう。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### データの作成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "では実際に`jpn-money`のデータを使いデータを整形しよう。まず月次データを読み込み`month`に割り当てる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>102.026510</td>\n",
       "      <td>147.486589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>101.526496</td>\n",
       "      <td>148.714911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>101.293089</td>\n",
       "      <td>149.341961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>101.346909</td>\n",
       "      <td>150.335605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>101.231809</td>\n",
       "      <td>151.327058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cpi       money\n",
       "                                  \n",
       "2020-08-01  102.026510  147.486589\n",
       "2020-09-01  101.526496  148.714911\n",
       "2020-10-01  101.293089  149.341961\n",
       "2020-11-01  101.346909  150.335605\n",
       "2020-12-01  101.231809  151.327058"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month = py4macro.data('jpn-money')\n",
    "month.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "いつもの通り`.info()`を使ってデータの内容を確認しよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 792 entries, 1955-01-01 to 2020-12-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   cpi     792 non-null    float64\n",
      " 1   money   792 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 18.6 KB\n"
     ]
    }
   ],
   "source": [
    "month.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "行ラベルが`DatetimeIndex`となっており，時系列データ用に設定されていることが分かる。\n",
    "\n",
    "四半期データに変換して変数`quarter`に割り当てることにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "quarter = month.resample('QS').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "これで３ヶ月の値の平均からなる四半期データを作成した事になる。確かめてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1955-01-01</th>\n",
       "      <td>17.243521</td>\n",
       "      <td>0.347656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-04-01</th>\n",
       "      <td>16.867311</td>\n",
       "      <td>0.357257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-07-01</th>\n",
       "      <td>17.036744</td>\n",
       "      <td>0.384201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-10-01</th>\n",
       "      <td>16.873758</td>\n",
       "      <td>0.400080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956-01-01</th>\n",
       "      <td>17.115098</td>\n",
       "      <td>0.394544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cpi     money\n",
       "                               \n",
       "1955-01-01  17.243521  0.347656\n",
       "1955-04-01  16.867311  0.357257\n",
       "1955-07-01  17.036744  0.384201\n",
       "1955-10-01  16.873758  0.400080\n",
       "1956-01-01  17.115098  0.394544"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "四半期の最初の日が行ラベルになっていることが分かる。同様に，年次データ作成しよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1955-01-01</th>\n",
       "      <td>17.005334</td>\n",
       "      <td>0.372299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956-01-01</th>\n",
       "      <td>17.072603</td>\n",
       "      <td>0.424722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957-01-01</th>\n",
       "      <td>17.601583</td>\n",
       "      <td>0.478435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-01-01</th>\n",
       "      <td>17.517496</td>\n",
       "      <td>0.505580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>17.707073</td>\n",
       "      <td>0.588486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cpi     money\n",
       "                               \n",
       "1955-01-01  17.005334  0.372299\n",
       "1956-01-01  17.072603  0.424722\n",
       "1957-01-01  17.601583  0.478435\n",
       "1958-01-01  17.517496  0.505580\n",
       "1959-01-01  17.707073  0.588486"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual = month.resample('YS').mean()\n",
    "annual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "１年の最初の日が行ラベルになっている。次に３年次データを作成しよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1955-01-01</th>\n",
       "      <td>17.226506</td>\n",
       "      <td>0.425152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-01-01</th>\n",
       "      <td>17.854861</td>\n",
       "      <td>0.599932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>20.666763</td>\n",
       "      <td>1.039100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>24.291933</td>\n",
       "      <td>1.641735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-01-01</th>\n",
       "      <td>28.080477</td>\n",
       "      <td>2.570458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cpi     money\n",
       "                               \n",
       "1955-01-01  17.226506  0.425152\n",
       "1958-01-01  17.854861  0.599932\n",
       "1961-01-01  20.666763  1.039100\n",
       "1964-01-01  24.291933  1.641735\n",
       "1967-01-01  28.080477  2.570458"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual3 = month.resample('3YS').mean()\n",
    "annual3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "行ラベルは3年間の最初の日になっている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次にインフレ率とマネーストックの増加率の変化を計算し，新たな列としてそれぞれの`DataFrame`に追加しよう。増加率の公式に従ってコードを書いても良いが，`DataFrame`のメソッド`.pct_change()`を紹介する。これは名前が示すように（percent changeの略）列の変化率を計算するメソッドである。ここで注意が必要な点は，`.pct_change()`はデフォルトで前期比の増加率を返す。例えば，次のコードは`cpi`の前月と比べた増加率を計算している。\n",
    "```\n",
    "month.loc[:,'cpi'].pct_change()\n",
    "```\n",
    "同年同期比の増加率を計算したい場合は，12ヶ月前の値と比べたいので引数に`12`を指定すれば良い。例として，`quarter`で`cpi`の同年同期比のインフレ率を計算する場合は次のようになる。\n",
    "```\n",
    "month.loc[:,'cpi'].pct_change(4)\n",
    "```\n",
    "以下では，デフォルトで`.pct_change()`を使い計算する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_lst = [month, quarter, annual, annual3]\n",
    "\n",
    "for df in df_lst:\n",
    "    \n",
    "    df['inflation'] = df.loc[:,'cpi'].pct_change()\n",
    "    df['money_growth'] = df.loc[:,'money'].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`month`を確認してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>money</th>\n",
       "      <th>inflation</th>\n",
       "      <th>money_growth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1955-01-01</th>\n",
       "      <td>17.224688</td>\n",
       "      <td>0.347029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-02-01</th>\n",
       "      <td>17.358845</td>\n",
       "      <td>0.350938</td>\n",
       "      <td>0.007789</td>\n",
       "      <td>0.011264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-03-01</th>\n",
       "      <td>17.147029</td>\n",
       "      <td>0.345003</td>\n",
       "      <td>-0.012202</td>\n",
       "      <td>-0.016910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-04-01</th>\n",
       "      <td>16.954310</td>\n",
       "      <td>0.353125</td>\n",
       "      <td>-0.011239</td>\n",
       "      <td>0.023541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955-05-01</th>\n",
       "      <td>16.713945</td>\n",
       "      <td>0.354401</td>\n",
       "      <td>-0.014177</td>\n",
       "      <td>0.003613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cpi     money  inflation  money_growth\n",
       "                                                        \n",
       "1955-01-01  17.224688  0.347029        NaN           NaN\n",
       "1955-02-01  17.358845  0.350938   0.007789      0.011264\n",
       "1955-03-01  17.147029  0.345003  -0.012202     -0.016910\n",
       "1955-04-01  16.954310  0.353125  -0.011239      0.023541\n",
       "1955-05-01  16.713945  0.354401  -0.014177      0.003613"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "行`1955-01-01`の`inflation`と`money_growth`の値は`NaN`となっている。これは前期の値がないためである。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 散布図とトレンド線"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`for`ループを使ってOLSの計算とプロットを同時におこなおう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_lst = ['月次データ','四半期データ','年次データ','3年次データ']       #1\n",
    "\n",
    "for df, t in zip(df_lst,title_lst):\n",
    "    \n",
    "    res = smf.ols('inflation ~ money_growth', data=df).fit()        #2\n",
    "    df['トレンド'] = res.fittedvalues                                #3\n",
    "    \n",
    "    ax_ = df.plot.scatter(x='money_growth', y='inflation')          #4\n",
    "    df.sort_values('トレンド').plot(x='money_growth', y='トレンド',    #5\n",
    "                                   color='r', ax=ax_)               #6\n",
    "    ax_.set_title(f'{t}\\n'                                          #7\n",
    "                  f'スロープ係数：{res.params[1]:.3f}\\n'               #8\n",
    "                  f'p値：{res.pvalues[1]:.3f}\\n'                     #9\n",
    "                  f'調整済み決定係数：{res.rsquared_adj:.3f}',         #10\n",
    "                  size=18, loc='left')                              #11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{admonition} コード説明\n",
    ":class: dropdown\n",
    "\n",
    "1. それぞれのプロットのタイトルのリスト。\n",
    "2. 最小二乗法の結果を変数`res`に割り当てる。\n",
    "3. `res.fittedvalues`はOLSの予測値であり，新たな列としてそれぞれの`DataFrame`に追加する。その際の列名を`トレンド`とする。\n",
    "4. 散布図を描き，生成される「軸」を`ax_`に割り当てる。\n",
    "5. トレンド線を描く。`.sort_values('トレンド')`を使って列`トレンド`を昇順に並び替える。\n",
    "6. `color='r'`は色を赤に指定する。\n",
    "7. `ax=ax_`はトレンド線を描く際，「軸」`ax_`を使うことをしてしている。`f-string`を使ってタイトルを`{t}`に代入している。\n",
    "8. `f-string`を使ってスロープ係数の推定値を代入している。\n",
    "    * `.pvalues`は推定値を抽出する`res`のメソッドであり，１番目の要素であるスロープ係数を`[1]`で指定している。\n",
    "    * `:.3f`は小数点第三位まで表示することを指定している。\n",
    "9. p値に関して(8)と同じことを行なっている。\n",
    "10. 調整済み決定係数に対して(8)と同じことを行なっている。\n",
    "11. `loc`はタイトルの位置を設定する引数。\n",
    "    * `'left'`は左寄せ\n",
    "    * `'right'`は右寄せ\n",
    "    * `'center'`は中央（デフォルト）\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "上の図とOLSの推定結果から次のことが分かる。\n",
    "* 全てのケースで統計的優位性は高い。\n",
    "* データの期間が長くなるとともに，スロープ係数の値が増加し，調整済み決定係数も高くなっている。\n",
    "\n",
    "これらのことからマネーストックの変化の影響は，より長い期間をかけてインフレへの影響が発生していることが伺える。この結果は，式[](eq:11-qtm_growth_long)は長期的に成立することと整合的であると言えそうだ。\n",
    "\n",
    "一方で，式[](eq:11-qtm_growth_long)は係数が`1`になることを予測しているが，上の4つのケースの係数は全て`1`よりも低い値となっている。この点を念頭に，次の節では日本だけではなく`170`国以上のデータを使い，問題を再検討することにする。また，OLS結果は因果関係を示しておらず単なる相関関係を表していることは念頭に置いておこう。\n",
    "\n",
    "```{note}\n",
    "４つの図の縦軸・横軸の値を比べると，データが長くなるにつれて値が大きくなることがわかる。期間が長くなると`cpi`と`M1`の増加率も上昇するためである。注意してほしいのは，増加率が上昇したために正の相関が強くなったという訳ではなく，増加率がより高くなっても正の相関は弱いまま，もしくは相関が存在しない場合もあり得る。図が示しているのは，データの期間が長くなるとより強い正の相関が「炙り出される」ということである。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 世界経済のパネルデータ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 説明"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "前節では日本のデータを使い，インフレ率に対するマネーストック増加率の影響が現れるには時間が掛かることを示した。一方でトレンド線の傾き（回帰分析のスロープ）は１よりも小さいが，式[](eq:11-qtm_growth_long)は線形であり，$m_t$の計数は１である。即ち，マネーストックの増加率が1％上昇するとインフレ率も1％増加するという予測である。データと理論予測の齟齬をどう考えれば良いだろうか。一つの問題はノイズである。年次データであっても3年次データであってもその期間に短期的なありとあらゆるランダムな要素（ノイズ）が含まれている。ノイズは正や負の両方の影響があると考えられ，その分変化が激しいと思われる。長期的な関係は，正と負の影響が相殺し，その結果残った関係と考えることができる。この考えをデータで捉えるためにデータ全体の平均を計算すれば良いことになる。しかし前節で使用した日本の1955年から2020年のデータを使い，インフレ率とマネーストトック増加率の平均を計算すると，標本の大きさは`１`となってしまう。これではどうしようもないので，国数を増やし，観測値を増やす必要がある。従って，ここでは世界経済のパネルデータを使い２変数の関係を探ることにする。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`py4macro`に含まれる`world-money`というデータ・セットを使うが，その内容は次のコードで確認できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    | `iso`:          ISO国コード\n",
      "    | `country`:      国名\n",
      "    | `year`:         年\n",
      "    | `income_group`: 世界銀行が定義する所得グループ\n",
      "    |   * High income\n",
      "    |   * Upper Middle income\n",
      "    |   * Lower Middle income\n",
      "    |   * Low income\n",
      "    | `money`:        マネーストック（M1）\n",
      "    | `deflator`:     GDPディフレーター\n",
      "    |\n",
      "    | * 年次データ\n",
      "    |\n",
      "    | ＜注意点＞\n",
      "    | * `money`と`deflator`が10年間以上連続で欠損値がない経済（177ヵ国）のみが含まれている。\n",
      "    | * 国によって含まれるデータの`year`が異なる。\n",
      "    | * 所得グループに関する情報\n",
      "    |   https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups\n",
      "    |\n",
      "    | ＜出典＞\n",
      "    | World Bank Development Indicators\n"
     ]
    }
   ],
   "source": [
    "py4macro.data('world-money',description=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "国によってデータが使える期間が異なることに注意しよう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "まず変数`world`にデータを割り当てる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>money</th>\n",
       "      <th>deflator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1994</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3.876590e+10</td>\n",
       "      <td>35.739368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1995</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>5.925260e+10</td>\n",
       "      <td>39.302820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1996</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>9.040510e+10</td>\n",
       "      <td>54.305537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1997</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>9.166720e+10</td>\n",
       "      <td>60.409306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1998</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>8.372854e+10</td>\n",
       "      <td>64.475001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iso  country  year         income_group         money   deflator\n",
       "0  ALB  Albania  1994  Upper middle income  3.876590e+10  35.739368\n",
       "1  ALB  Albania  1995  Upper middle income  5.925260e+10  39.302820\n",
       "2  ALB  Albania  1996  Upper middle income  9.040510e+10  54.305537\n",
       "3  ALB  Albania  1997  Upper middle income  9.166720e+10  60.409306\n",
       "4  ALB  Albania  1998  Upper middle income  8.372854e+10  64.475001"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = py4macro.data('world-money')\n",
    "world.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "いつも通り`.info()`を使って内容を確かめてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6584 entries, 0 to 6583\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   iso           6584 non-null   object \n",
      " 1   country       6584 non-null   object \n",
      " 2   year          6584 non-null   int64  \n",
      " 3   income_group  6584 non-null   object \n",
      " 4   money         6584 non-null   float64\n",
      " 5   deflator      6584 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 308.8+ KB\n"
     ]
    }
   ],
   "source": [
    "world.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "欠損値はないことが確認できる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 変化率の計算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`world`には経済ごとに10年以上に渡ってインフレ率とマネーストック増加率が含まれている。国ごとの変数の変化率を計算するには，以前紹介した`.pivot()`を使うことも可能だが，少し回りくどい計算になっている。ここでは異なる方法として`.groupby()`を紹介する。`.groupby()`はグループ内で何らかの計算をする際に非常に便利なメソッドである。次のステップに従って説明する。\n",
    "1. `DataFram`をどの変数でグループ化するかを指定し，グループ化計算用のオブジェクトを用意する。\n",
    "    * ここでは経済ごとの平均を計算したいので，国`iso`（もしくは`country`）でグループ化する。\n",
    "1. グループ計算したい列を選ぶ。\n",
    "    * ここでは`money`と`deflation`となる。\n",
    "1. どのような計算をしたいのかを指定する。\n",
    "    * ここでは増加率なので`.pct_change()`を使う。\n",
    "\n",
    "**＜ステップ１＞**<br>\n",
    "グループ化用のオブジェクトの作成するためには`DataFrame`のメソッド`.groupby()`を使い，その引数にグループ化用の列を指定する。ここでは`world`を`iso`でグループ化した変数`world_group`に割り当てる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x14a61c750>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_group = world.groupby('iso')\n",
    "world_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`DataFrameGroupBy object`が生成されたことを知らせるメッセージである。このオブジェクトは`world`自体をグループ化計算用に変換したものであり，`DataFrame`のように`.loc[]`などのメソッドは用意されていないので注意しよう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**＜ステップ２＞**<br>\n",
    "グループ計算したいのは`money`と`deflator`である。同時に指定しても構わないが，ここでは一つずつ指定することにする。例として`money`を考えよう。列を指定するには`[]`を使う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x14a686b90>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_group['money']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`SeriesGroupBy object`が生成されたことを知らせるメッセージである。ステップ１で生成された`DataFrameGroupBy object`から`money`の箇所を取り出したグループ計算用オブジェクトである。`Series`となっていることから分かるように，`iso`でグループ化され列`money`専用のグループ計算オブジェクトである。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**＜ステップ３＞**<br>\n",
    "グループ計算に平均を使いたいので，ステップ２のオブジェクトに`.pct_change()`をつか加えるだけである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1       0.528472\n",
       "2       0.525758\n",
       "3       0.013960\n",
       "4      -0.086603\n",
       "          ...   \n",
       "6579    1.700756\n",
       "6580   -0.538285\n",
       "6581   -0.547881\n",
       "6582    0.479342\n",
       "6583    0.936070\n",
       "Name: money, Length: 6584, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_group['money'].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "返されたのは国ごとに計算されたマネーストック増加率である。`Series`として返されているが，行の並びは`world`と同じである。従って，次のコードでマネーストック増加率の列を`world`に追加できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>money</th>\n",
       "      <th>deflator</th>\n",
       "      <th>money_growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1994</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3.876590e+10</td>\n",
       "      <td>35.739368</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1995</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>5.925260e+10</td>\n",
       "      <td>39.302820</td>\n",
       "      <td>52.847219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1996</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>9.040510e+10</td>\n",
       "      <td>54.305537</td>\n",
       "      <td>52.575752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1997</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>9.166720e+10</td>\n",
       "      <td>60.409306</td>\n",
       "      <td>1.396050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1998</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>8.372854e+10</td>\n",
       "      <td>64.475001</td>\n",
       "      <td>-8.660303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iso  country  year         income_group         money   deflator  \\\n",
       "0  ALB  Albania  1994  Upper middle income  3.876590e+10  35.739368   \n",
       "1  ALB  Albania  1995  Upper middle income  5.925260e+10  39.302820   \n",
       "2  ALB  Albania  1996  Upper middle income  9.040510e+10  54.305537   \n",
       "3  ALB  Albania  1997  Upper middle income  9.166720e+10  60.409306   \n",
       "4  ALB  Albania  1998  Upper middle income  8.372854e+10  64.475001   \n",
       "\n",
       "   money_growth  \n",
       "0           NaN  \n",
       "1     52.847219  \n",
       "2     52.575752  \n",
       "3      1.396050  \n",
       "4     -8.660303  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world['money_growth'] = world_group['money'].pct_change()*100\n",
    "world.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "列`money_growth`が最後に追加さている。Albaniaの最初の行である0番目の行は`NaN`になっているが，増加率を計算する際に発生している。同様に，全ての国の最初の行には`NaN`が入っている（確かめてみよう）。\n",
    "\n",
    "次にインフレ率を計算する。次のコードは上で説明した手順を１行で書いている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "world['inflation'] = world_group['deflator'].pct_change()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "試しに，日本のデータだけを抽出してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>money</th>\n",
       "      <th>deflator</th>\n",
       "      <th>money_growth</th>\n",
       "      <th>inflation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1961</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.102000e+12</td>\n",
       "      <td>24.337971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1962</td>\n",
       "      <td>High income</td>\n",
       "      <td>6.090000e+12</td>\n",
       "      <td>25.360063</td>\n",
       "      <td>48.464164</td>\n",
       "      <td>4.199578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1963</td>\n",
       "      <td>High income</td>\n",
       "      <td>7.702000e+12</td>\n",
       "      <td>26.757522</td>\n",
       "      <td>26.469622</td>\n",
       "      <td>5.510469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1964</td>\n",
       "      <td>High income</td>\n",
       "      <td>8.704000e+12</td>\n",
       "      <td>28.183351</td>\n",
       "      <td>13.009608</td>\n",
       "      <td>5.328706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1965</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.028700e+13</td>\n",
       "      <td>29.631090</td>\n",
       "      <td>18.187040</td>\n",
       "      <td>5.136857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1966</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.171600e+13</td>\n",
       "      <td>31.104013</td>\n",
       "      <td>13.891319</td>\n",
       "      <td>4.970869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1967</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.336900e+13</td>\n",
       "      <td>32.813300</td>\n",
       "      <td>14.108911</td>\n",
       "      <td>5.495390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1968</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.515500e+13</td>\n",
       "      <td>34.427341</td>\n",
       "      <td>13.359264</td>\n",
       "      <td>4.918863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3030</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1969</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.828200e+13</td>\n",
       "      <td>35.953642</td>\n",
       "      <td>20.633454</td>\n",
       "      <td>4.433399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3031</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1970</td>\n",
       "      <td>High income</td>\n",
       "      <td>2.421400e+13</td>\n",
       "      <td>44.213162</td>\n",
       "      <td>32.447216</td>\n",
       "      <td>22.972693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3032</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1971</td>\n",
       "      <td>High income</td>\n",
       "      <td>3.097200e+13</td>\n",
       "      <td>46.464210</td>\n",
       "      <td>27.909474</td>\n",
       "      <td>5.091352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3033</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1972</td>\n",
       "      <td>High income</td>\n",
       "      <td>3.857900e+13</td>\n",
       "      <td>49.068260</td>\n",
       "      <td>24.560894</td>\n",
       "      <td>5.604422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3034</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1973</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.561100e+13</td>\n",
       "      <td>55.302577</td>\n",
       "      <td>18.227533</td>\n",
       "      <td>12.705396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3035</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1974</td>\n",
       "      <td>High income</td>\n",
       "      <td>5.106500e+13</td>\n",
       "      <td>66.811071</td>\n",
       "      <td>11.957642</td>\n",
       "      <td>20.810050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3036</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1975</td>\n",
       "      <td>High income</td>\n",
       "      <td>5.684700e+13</td>\n",
       "      <td>71.606396</td>\n",
       "      <td>11.322824</td>\n",
       "      <td>7.177441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1976</td>\n",
       "      <td>High income</td>\n",
       "      <td>6.381800e+13</td>\n",
       "      <td>77.340555</td>\n",
       "      <td>12.262740</td>\n",
       "      <td>8.007886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3038</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1977</td>\n",
       "      <td>High income</td>\n",
       "      <td>6.893500e+13</td>\n",
       "      <td>82.560351</td>\n",
       "      <td>8.018114</td>\n",
       "      <td>6.749107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3039</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1978</td>\n",
       "      <td>High income</td>\n",
       "      <td>7.797000e+13</td>\n",
       "      <td>86.361230</td>\n",
       "      <td>13.106550</td>\n",
       "      <td>4.603758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1979</td>\n",
       "      <td>High income</td>\n",
       "      <td>8.071200e+13</td>\n",
       "      <td>88.737573</td>\n",
       "      <td>3.516737</td>\n",
       "      <td>2.751631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1980</td>\n",
       "      <td>High income</td>\n",
       "      <td>7.930300e+13</td>\n",
       "      <td>93.563108</td>\n",
       "      <td>-1.745713</td>\n",
       "      <td>5.437984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1981</td>\n",
       "      <td>High income</td>\n",
       "      <td>8.678700e+13</td>\n",
       "      <td>96.301543</td>\n",
       "      <td>9.437222</td>\n",
       "      <td>2.926833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1982</td>\n",
       "      <td>High income</td>\n",
       "      <td>9.193400e+13</td>\n",
       "      <td>97.981975</td>\n",
       "      <td>5.930612</td>\n",
       "      <td>1.744969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1983</td>\n",
       "      <td>High income</td>\n",
       "      <td>9.198900e+13</td>\n",
       "      <td>98.908551</td>\n",
       "      <td>0.059826</td>\n",
       "      <td>0.945660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1984</td>\n",
       "      <td>High income</td>\n",
       "      <td>9.812700e+13</td>\n",
       "      <td>100.365852</td>\n",
       "      <td>6.672537</td>\n",
       "      <td>1.473382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1985</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.010060e+14</td>\n",
       "      <td>101.630611</td>\n",
       "      <td>2.933953</td>\n",
       "      <td>1.260150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3047</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1986</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.112170e+14</td>\n",
       "      <td>103.269102</td>\n",
       "      <td>10.109300</td>\n",
       "      <td>1.612202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3048</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1987</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.166330e+14</td>\n",
       "      <td>103.106047</td>\n",
       "      <td>4.869759</td>\n",
       "      <td>-0.157894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3049</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1988</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.262110e+14</td>\n",
       "      <td>103.750762</td>\n",
       "      <td>8.212084</td>\n",
       "      <td>0.625293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1989</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.299050e+14</td>\n",
       "      <td>105.938752</td>\n",
       "      <td>2.926845</td>\n",
       "      <td>2.108890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3051</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1990</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.350370e+14</td>\n",
       "      <td>108.698779</td>\n",
       "      <td>3.950579</td>\n",
       "      <td>2.605305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3052</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1991</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.428830e+14</td>\n",
       "      <td>111.881314</td>\n",
       "      <td>5.810259</td>\n",
       "      <td>2.927848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3053</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1992</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.477900e+14</td>\n",
       "      <td>113.745972</td>\n",
       "      <td>3.434278</td>\n",
       "      <td>1.666639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3054</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1993</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.575810e+14</td>\n",
       "      <td>114.392473</td>\n",
       "      <td>6.624941</td>\n",
       "      <td>0.568373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3055</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1994</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.641370e+14</td>\n",
       "      <td>114.696203</td>\n",
       "      <td>4.160400</td>\n",
       "      <td>0.265515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3056</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1995</td>\n",
       "      <td>High income</td>\n",
       "      <td>1.907660e+14</td>\n",
       "      <td>114.084345</td>\n",
       "      <td>16.223642</td>\n",
       "      <td>-0.533460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3057</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1996</td>\n",
       "      <td>High income</td>\n",
       "      <td>2.087810e+14</td>\n",
       "      <td>113.517931</td>\n",
       "      <td>9.443507</td>\n",
       "      <td>-0.496487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1997</td>\n",
       "      <td>High income</td>\n",
       "      <td>2.498136e+14</td>\n",
       "      <td>114.089868</td>\n",
       "      <td>19.653417</td>\n",
       "      <td>0.503830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3059</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1998</td>\n",
       "      <td>High income</td>\n",
       "      <td>2.616490e+14</td>\n",
       "      <td>114.038391</td>\n",
       "      <td>4.737692</td>\n",
       "      <td>-0.045120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1999</td>\n",
       "      <td>High income</td>\n",
       "      <td>2.903745e+14</td>\n",
       "      <td>112.545067</td>\n",
       "      <td>10.978639</td>\n",
       "      <td>-1.309492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3061</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2000</td>\n",
       "      <td>High income</td>\n",
       "      <td>3.001829e+14</td>\n",
       "      <td>110.987797</td>\n",
       "      <td>3.377845</td>\n",
       "      <td>-1.383686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3062</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2001</td>\n",
       "      <td>High income</td>\n",
       "      <td>3.111430e+14</td>\n",
       "      <td>109.761918</td>\n",
       "      <td>3.651141</td>\n",
       "      <td>-1.104517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3063</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2002</td>\n",
       "      <td>High income</td>\n",
       "      <td>3.796386e+14</td>\n",
       "      <td>108.161276</td>\n",
       "      <td>22.014186</td>\n",
       "      <td>-1.458286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3064</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2003</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.506541e+14</td>\n",
       "      <td>106.412328</td>\n",
       "      <td>18.706080</td>\n",
       "      <td>-1.616981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3065</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2004</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.695480e+14</td>\n",
       "      <td>105.241010</td>\n",
       "      <td>4.192550</td>\n",
       "      <td>-1.100736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2005</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.944894e+14</td>\n",
       "      <td>104.149203</td>\n",
       "      <td>5.311789</td>\n",
       "      <td>-1.037435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2006</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.953890e+14</td>\n",
       "      <td>103.229172</td>\n",
       "      <td>0.181925</td>\n",
       "      <td>-0.883378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2007</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.967105e+14</td>\n",
       "      <td>102.476136</td>\n",
       "      <td>0.266760</td>\n",
       "      <td>-0.729480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2008</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.904771e+14</td>\n",
       "      <td>101.470952</td>\n",
       "      <td>-1.254936</td>\n",
       "      <td>-0.980895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2009</td>\n",
       "      <td>High income</td>\n",
       "      <td>4.971079e+14</td>\n",
       "      <td>100.850688</td>\n",
       "      <td>1.351908</td>\n",
       "      <td>-0.611273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2010</td>\n",
       "      <td>High income</td>\n",
       "      <td>5.133780e+14</td>\n",
       "      <td>98.939403</td>\n",
       "      <td>3.272951</td>\n",
       "      <td>-1.895164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2011</td>\n",
       "      <td>High income</td>\n",
       "      <td>5.412710e+14</td>\n",
       "      <td>97.282835</td>\n",
       "      <td>5.433229</td>\n",
       "      <td>-1.674326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2012</td>\n",
       "      <td>High income</td>\n",
       "      <td>5.607333e+14</td>\n",
       "      <td>96.541973</td>\n",
       "      <td>3.595666</td>\n",
       "      <td>-0.761555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2013</td>\n",
       "      <td>High income</td>\n",
       "      <td>5.914823e+14</td>\n",
       "      <td>96.220317</td>\n",
       "      <td>5.483712</td>\n",
       "      <td>-0.333177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2014</td>\n",
       "      <td>High income</td>\n",
       "      <td>6.203437e+14</td>\n",
       "      <td>97.899664</td>\n",
       "      <td>4.879504</td>\n",
       "      <td>1.745314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso country  year income_group         money    deflator  money_growth  \\\n",
       "3022  JPN   Japan  1961  High income  4.102000e+12   24.337971           NaN   \n",
       "3023  JPN   Japan  1962  High income  6.090000e+12   25.360063     48.464164   \n",
       "3024  JPN   Japan  1963  High income  7.702000e+12   26.757522     26.469622   \n",
       "3025  JPN   Japan  1964  High income  8.704000e+12   28.183351     13.009608   \n",
       "3026  JPN   Japan  1965  High income  1.028700e+13   29.631090     18.187040   \n",
       "3027  JPN   Japan  1966  High income  1.171600e+13   31.104013     13.891319   \n",
       "3028  JPN   Japan  1967  High income  1.336900e+13   32.813300     14.108911   \n",
       "3029  JPN   Japan  1968  High income  1.515500e+13   34.427341     13.359264   \n",
       "3030  JPN   Japan  1969  High income  1.828200e+13   35.953642     20.633454   \n",
       "3031  JPN   Japan  1970  High income  2.421400e+13   44.213162     32.447216   \n",
       "3032  JPN   Japan  1971  High income  3.097200e+13   46.464210     27.909474   \n",
       "3033  JPN   Japan  1972  High income  3.857900e+13   49.068260     24.560894   \n",
       "3034  JPN   Japan  1973  High income  4.561100e+13   55.302577     18.227533   \n",
       "3035  JPN   Japan  1974  High income  5.106500e+13   66.811071     11.957642   \n",
       "3036  JPN   Japan  1975  High income  5.684700e+13   71.606396     11.322824   \n",
       "3037  JPN   Japan  1976  High income  6.381800e+13   77.340555     12.262740   \n",
       "3038  JPN   Japan  1977  High income  6.893500e+13   82.560351      8.018114   \n",
       "3039  JPN   Japan  1978  High income  7.797000e+13   86.361230     13.106550   \n",
       "3040  JPN   Japan  1979  High income  8.071200e+13   88.737573      3.516737   \n",
       "3041  JPN   Japan  1980  High income  7.930300e+13   93.563108     -1.745713   \n",
       "3042  JPN   Japan  1981  High income  8.678700e+13   96.301543      9.437222   \n",
       "3043  JPN   Japan  1982  High income  9.193400e+13   97.981975      5.930612   \n",
       "3044  JPN   Japan  1983  High income  9.198900e+13   98.908551      0.059826   \n",
       "3045  JPN   Japan  1984  High income  9.812700e+13  100.365852      6.672537   \n",
       "3046  JPN   Japan  1985  High income  1.010060e+14  101.630611      2.933953   \n",
       "3047  JPN   Japan  1986  High income  1.112170e+14  103.269102     10.109300   \n",
       "3048  JPN   Japan  1987  High income  1.166330e+14  103.106047      4.869759   \n",
       "3049  JPN   Japan  1988  High income  1.262110e+14  103.750762      8.212084   \n",
       "3050  JPN   Japan  1989  High income  1.299050e+14  105.938752      2.926845   \n",
       "3051  JPN   Japan  1990  High income  1.350370e+14  108.698779      3.950579   \n",
       "3052  JPN   Japan  1991  High income  1.428830e+14  111.881314      5.810259   \n",
       "3053  JPN   Japan  1992  High income  1.477900e+14  113.745972      3.434278   \n",
       "3054  JPN   Japan  1993  High income  1.575810e+14  114.392473      6.624941   \n",
       "3055  JPN   Japan  1994  High income  1.641370e+14  114.696203      4.160400   \n",
       "3056  JPN   Japan  1995  High income  1.907660e+14  114.084345     16.223642   \n",
       "3057  JPN   Japan  1996  High income  2.087810e+14  113.517931      9.443507   \n",
       "3058  JPN   Japan  1997  High income  2.498136e+14  114.089868     19.653417   \n",
       "3059  JPN   Japan  1998  High income  2.616490e+14  114.038391      4.737692   \n",
       "3060  JPN   Japan  1999  High income  2.903745e+14  112.545067     10.978639   \n",
       "3061  JPN   Japan  2000  High income  3.001829e+14  110.987797      3.377845   \n",
       "3062  JPN   Japan  2001  High income  3.111430e+14  109.761918      3.651141   \n",
       "3063  JPN   Japan  2002  High income  3.796386e+14  108.161276     22.014186   \n",
       "3064  JPN   Japan  2003  High income  4.506541e+14  106.412328     18.706080   \n",
       "3065  JPN   Japan  2004  High income  4.695480e+14  105.241010      4.192550   \n",
       "3066  JPN   Japan  2005  High income  4.944894e+14  104.149203      5.311789   \n",
       "3067  JPN   Japan  2006  High income  4.953890e+14  103.229172      0.181925   \n",
       "3068  JPN   Japan  2007  High income  4.967105e+14  102.476136      0.266760   \n",
       "3069  JPN   Japan  2008  High income  4.904771e+14  101.470952     -1.254936   \n",
       "3070  JPN   Japan  2009  High income  4.971079e+14  100.850688      1.351908   \n",
       "3071  JPN   Japan  2010  High income  5.133780e+14   98.939403      3.272951   \n",
       "3072  JPN   Japan  2011  High income  5.412710e+14   97.282835      5.433229   \n",
       "3073  JPN   Japan  2012  High income  5.607333e+14   96.541973      3.595666   \n",
       "3074  JPN   Japan  2013  High income  5.914823e+14   96.220317      5.483712   \n",
       "3075  JPN   Japan  2014  High income  6.203437e+14   97.899664      4.879504   \n",
       "\n",
       "      inflation  \n",
       "3022        NaN  \n",
       "3023   4.199578  \n",
       "3024   5.510469  \n",
       "3025   5.328706  \n",
       "3026   5.136857  \n",
       "3027   4.970869  \n",
       "3028   5.495390  \n",
       "3029   4.918863  \n",
       "3030   4.433399  \n",
       "3031  22.972693  \n",
       "3032   5.091352  \n",
       "3033   5.604422  \n",
       "3034  12.705396  \n",
       "3035  20.810050  \n",
       "3036   7.177441  \n",
       "3037   8.007886  \n",
       "3038   6.749107  \n",
       "3039   4.603758  \n",
       "3040   2.751631  \n",
       "3041   5.437984  \n",
       "3042   2.926833  \n",
       "3043   1.744969  \n",
       "3044   0.945660  \n",
       "3045   1.473382  \n",
       "3046   1.260150  \n",
       "3047   1.612202  \n",
       "3048  -0.157894  \n",
       "3049   0.625293  \n",
       "3050   2.108890  \n",
       "3051   2.605305  \n",
       "3052   2.927848  \n",
       "3053   1.666639  \n",
       "3054   0.568373  \n",
       "3055   0.265515  \n",
       "3056  -0.533460  \n",
       "3057  -0.496487  \n",
       "3058   0.503830  \n",
       "3059  -0.045120  \n",
       "3060  -1.309492  \n",
       "3061  -1.383686  \n",
       "3062  -1.104517  \n",
       "3063  -1.458286  \n",
       "3064  -1.616981  \n",
       "3065  -1.100736  \n",
       "3066  -1.037435  \n",
       "3067  -0.883378  \n",
       "3068  -0.729480  \n",
       "3069  -0.980895  \n",
       "3070  -0.611273  \n",
       "3071  -1.895164  \n",
       "3072  -1.674326  \n",
       "3073  -0.761555  \n",
       "3074  -0.333177  \n",
       "3075   1.745314  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.query('iso==\"JPN\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "上で説明したように，列`money_growth`と`inflation`の0番目の行の要素は`NaN`となっている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### ハイパーインフレ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "ハイパーインフレの確固たる定義はないが，Mankiwの教科書「マクロ経済学」では年率50％以上と定義している。この定義に基づき，ハイパーインフレは観測値の何％を占めるかを計算してみよう。まず`inflation`で`NaN`ではない行の数を数える。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6407"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notna = world.loc[:,'inflation'].notna().sum()\n",
    "notna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{admonition} コードの説明\n",
    ":class: dropdown\n",
    "\n",
    "欠損値である`NaN`は`na`（not available）とも呼ばれる。メソッド`.notna()`は文字通り`na`ではない要素には`True`を`na`である要素には`False`を返す。`.sum()`は`True`の数を合計している。\n",
    "```\n",
    "\n",
    "`inflation`の値が`NaN`ではない行数を確認した。次に`inflation`が50％以上の行数を数えてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper = len( world.query('inflation >= 50') )\n",
    "hyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "観測値の4.68％でハイパーインフレが発生している。\n"
     ]
    }
   ],
   "source": [
    "print(f'観測値の{100*hyper/notna:.2f}％でハイパーインフレが発生している。')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次に`inflation`の上位5ヵ国を表示してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>money</th>\n",
       "      <th>deflator</th>\n",
       "      <th>money_growth</th>\n",
       "      <th>inflation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>COD</td>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>1994</td>\n",
       "      <td>Low income</td>\n",
       "      <td>3725130.0</td>\n",
       "      <td>0.001219</td>\n",
       "      <td>5635.381062</td>\n",
       "      <td>26765.858252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4277</th>\n",
       "      <td>NIC</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>1988</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>33480.0</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>11673.429423</td>\n",
       "      <td>13611.634819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>BOL</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>1985</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>111800000.0</td>\n",
       "      <td>17.094034</td>\n",
       "      <td>3587.821612</td>\n",
       "      <td>12338.622324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4719</th>\n",
       "      <td>PER</td>\n",
       "      <td>Peru</td>\n",
       "      <td>1990</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>707331500.0</td>\n",
       "      <td>3.608738</td>\n",
       "      <td>6724.820224</td>\n",
       "      <td>6261.239559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4279</th>\n",
       "      <td>NIC</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>1990</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>52778530.0</td>\n",
       "      <td>0.223530</td>\n",
       "      <td>6286.714344</td>\n",
       "      <td>5016.107950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso           country  year         income_group        money  \\\n",
       "1413  COD  Congo, Dem. Rep.  1994           Low income    3725130.0   \n",
       "4277  NIC         Nicaragua  1988  Lower middle income      33480.0   \n",
       "680   BOL           Bolivia  1985  Lower middle income  111800000.0   \n",
       "4719  PER              Peru  1990  Upper middle income  707331500.0   \n",
       "4279  NIC         Nicaragua  1990  Lower middle income   52778530.0   \n",
       "\n",
       "       deflator  money_growth     inflation  \n",
       "1413   0.001219   5635.381062  26765.858252  \n",
       "4277   0.000091  11673.429423  13611.634819  \n",
       "680   17.094034   3587.821612  12338.622324  \n",
       "4719   3.608738   6724.820224   6261.239559  \n",
       "4279   0.223530   6286.714344   5016.107950  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.sort_values('inflation', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "トップはコンゴ共和国（COD）の年率26,766％！しかし，この数字はあまりピンとこないかもしれないので，次式を使って一日のインフレ率に換算してみよう。\n",
    "\n",
    "$$\n",
    "(1+g_{{日}})^{365}=1+g_{\\text{年}}\n",
    "\\quad\\Rightarrow\\quad\n",
    "g_{{日}}=(1+g_{\\text{年}})^{\\frac{1}{365}}-1\n",
    "$$\n",
    "\n",
    "ここで$g_{{年}}$は年率のインフレ率であり，$g_{{日}}$は1日当たりのインフレ率。この式を使い１に当たりの平均インフレ率を計算してみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1413    1.544252\n",
       "Name: inflation, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_cod = world.query('(iso==\"COD\") & (year==1994)').loc[:,'inflation']\n",
    "\n",
    "inflation_cod_day = 100*( \n",
    "    ( 1+inflation_cod/100 )**( 1/365 )-1 \n",
    ")\n",
    "\n",
    "inflation_cod_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1日平均約`1.54`％のインフレ率となる。次に１ヶ月間のインフレ率を計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1413    59.379842\n",
       "Name: inflation, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*( \n",
    "    ( 1+inflation_cod/100 )**( 1/12 )-1 \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "月平均では`60`％近くのインフレ率となる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1日平均約`1.54`％をもう少し身近に感じられるように，日本と比較してみよう。バブル景気崩壊直後の`1995`年と`2023`年の消費者物価指数（総合）は、それぞれ`95.9`と`105.6`である（[このページのデータを参照](https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00200573&tstat=000001150147&cycle=0&tclass1=000001150151&tclass2=000001150152&tclass3=000001150153&tclass4=000001150154&tclass5val=0)）。年平均（幾何平均）は次のようになる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1995-2023年の年平均インフレ率（消費者物価指数）：0.34％\n"
     ]
    }
   ],
   "source": [
    "jp_inflation = 100 * ( (105.6 / 95.9)**( 1/(2023-1995) ) - 1 )\n",
    "print(f'1995-2023年の年平均インフレ率（消費者物価指数）：{jp_inflation:.2f}％')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "日本のインフレ率も非常に低いと言わざるを得ないが、日本の`29`年間の**年**平均インフレ率よりも高い率で**毎日**インフレが発生するのは、大変な事だろうと想像に難しくないのではないだろうか。別の考え方として，物価が２倍になるには何日かかるかを考えてみよう。$t$日後に物価は２倍になるとすると，次式が成立する。\n",
    "\n",
    "$$\n",
    "2=(1+g_{\\text{日}})^t\n",
    "\\quad\\Rightarrow\\quad\n",
    "t=\\frac{\\log(2)}{\\log(1+g_{\\text{日}})}\n",
    "$$\n",
    "\n",
    "この式を使って$t$を計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1413    45.231317\n",
       "Name: inflation, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(2) / np.log( 1+inflation_cod_day/100 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "約`45`日間で物価は`2`倍になることが分かる。例えば、`10`万円するPCやスマホが、`45`日後には`20`万円になっているという状況である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### プロット"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "全てのデータを使って散布図をプロットしトレンドを計算してみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "world.plot.scatter(x='money_growth', y='inflation')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{admonition} コードの説明\n",
    ":class: dropdown\n",
    "\n",
    "`world`には`inflation`と`money_growth`が`NaN`となっている行が含まれるが，上の図では自動的に省かれる。`world.dropna().plot()`としても図は変わらない。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "横軸と縦軸の値（％）を確認してみると分かるが非常に大きい。ノイズの影響により変化が非常に激しいためである。トレンドのスロープを計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "標本の大きさ：6407\n",
      "調整済み決定係数：0.537\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>   -9.6447</td> <td>    3.843</td> <td>   -2.510</td> <td> 0.012</td> <td>  -17.178</td> <td>   -2.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>money_growth</th> <td>    1.2802</td> <td>    0.015</td> <td>   86.259</td> <td> 0.000</td> <td>    1.251</td> <td>    1.309</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccccc}\n",
       "\\toprule\n",
       "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}     &      -9.6447  &        3.843     &    -2.510  &         0.012        &      -17.178    &       -2.112     \\\\\n",
       "\\textbf{money\\_growth} &       1.2802  &        0.015     &    86.259  &         0.000        &        1.251    &        1.309     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_world = smf.ols('inflation ~ money_growth', data=world).fit()\n",
    "print(f'標本の大きさ：{int(res_world.nobs)}')\n",
    "print(f'調整済み決定係数：{res_world.rsquared_adj:.3f}')\n",
    "res_world.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "トレンドのスロープは1.28であり，1に近いがノイズの影響が大きいようである。\n",
    "\n",
    "この結果と比較したいのが，次のケースである。\n",
    "* それぞれの国で`inflation`と`money_growth`の平均を計算し，その散布図をプロットするとともに傾きを計算する。\n",
    "\n",
    "まず，それぞれの経済の２変数の平均を計算するが，一つ注意点がある。`world`に`inflation`と`money_growth`があるので，`.mean()`を使って平均を計算すれば良いと思うかもしれない。しかし`.mean()`は算術平均であり，計算したいのは増加率の平均なので可能であれば幾何平均を使うべきである。残念ながら，`DataFrame`には幾何平均のメソッドが良いされて良いないので，次のように`for`ループで計算することにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "money_growth_mean_lst = []                   # 1\n",
    "inflation_mean_lst = []                      # 2\n",
    "iso_lst = []                                 # 3 \n",
    "country_lst = []                             # 4\n",
    "income_group_lst = []                        # 5\n",
    "\n",
    "for c in world.loc[:,'iso'].unique():         # 6\n",
    "    df = world.query('iso==@c').reset_index() # 7\n",
    "    n = len(df)                               # 9\n",
    "    \n",
    "    money_growth_mean = 100*(                 # 9\n",
    "        (df.loc[n-1,'money']/df.loc[0,'money'])**(1/(n-1))-1\n",
    "    )\n",
    "    inflation_mean = 100*(                    # 10\n",
    "        (df.loc[n-1,'deflator']/df.loc[0,'deflator'])**(1/(n-1))-1\n",
    "    )\n",
    "                                              # 11\n",
    "    money_growth_mean_lst.append(money_growth_mean)\n",
    "    inflation_mean_lst.append(inflation_mean)\n",
    "    iso_lst.append(c)\n",
    "                                              # 12\n",
    "    country_lst.append(df.loc[:,'country'].unique()[0])\n",
    "    \n",
    "                                              # 13\n",
    "    income_group_lst.append(df.loc[:,'income_group'].unique()[0])\n",
    "    \n",
    "                                              # 14\n",
    "world_mean = pd.DataFrame({'country':country_lst,\n",
    "                           'income_group':income_group_lst,\n",
    "                           'money_growth_mean':money_growth_mean_lst,\n",
    "                           'inflation_mean':inflation_mean_lst,\n",
    "                           'iso':iso_lst}).set_index('iso')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{admonition} コードの説明\n",
    ":class: dropdown\n",
    "\n",
    "1. マネーストックの平均増加率を格納する空のリスト。\n",
    "2. 平均インフレ率を格納する空のリスト。\n",
    "3. 国のisoを格納する空のリスト。\n",
    "4. 国名を格納するリスト。\n",
    "5. 所得グループ名を格納するリスト。\n",
    "6. データにある経済全てに対しての`for`ループを開始\n",
    "    * `world.loc[:,'iso']`は列`iso`を抽出し，`.unique()`を使って経済のリストを作成する。\n",
    "7. `.query('iso==@c')`で`c`国の行だけを抽出し，`reset_index()`で行インデックスを振り直す。`c`国の`DataFrame`を`df`に割り当てる。\n",
    "8. `df`の行数を`n`に割り当てる。\n",
    "9. `df`の列`money`の最初と最後の行の値を使って平均増加率を計算する。`100*`で％表示にする。\n",
    "10. `df`の列`deflator`の最初と最後の行の値を使って平均インフレ率を計算する。`100*`で％表示にする。\n",
    "11. `money_growth_mean`，`inflation_mean`，`c`をそれぞれ対応するリストに追加する。\n",
    "12. 国名を`country_list`に追加する。\n",
    "    * `df.loc[:,'country']`で列`country`を抽出し，`.unique()`で国名が入る`array`が返される。`[0]`はその0番目の要素を抽出している。\n",
    "13. 所得グループ名を`income_group_list`に追加する。\n",
    "    * `df.loc[:,'income_group']`で列`income_group`を抽出し，`.unique()`でグループ名が入る`array`が返される。`[0]`はその0番目の要素を抽出している。\n",
    "14. `money_growth_mean_list`，`inflation_mean_list`，`country_list`を使い`DataFrame`を作成し`world_mean`に割り当てる。`.set_index('iso')`は`iso`を行ラベルに設定している。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "````{tip}\n",
    "実は`.groupby()`使って幾何平均を計算することも可能である。例えば，次のコードでマネーストック増加率の幾何平均を計算できる。\n",
    "```\n",
    "from scipy.stats import gmean            # 1\n",
    "\n",
    "world.groupby('iso')['money'].agg(gmean) # 2\n",
    "```\n",
    "簡単なコードで良いが，(1)にある関数を導入する必要があり，また(2)の`.agg()`を使う必要がある。上のコードは`for`ループに慣れることを一つの目的としている。\n",
    "````\n",
    "\n",
    "平均でハイパーインフレが発生している国は何ヵ国なるのか計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177ヵ国中7ヵ国でハイパーインフレが発生している。\n"
     ]
    }
   ],
   "source": [
    "hyper = ( world_mean.loc[:,'inflation_mean'] >= 50 ).sum()\n",
    "\n",
    "print(f'{len(world_mean)}ヵ国中{hyper}ヵ国でハイパーインフレが発生している。')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{admonition} コードの説明\n",
    ":class: dropdown\n",
    "\n",
    "* `world_mean.loc[:,'inflation_mean']`で`inflation_mean`の列を抽出。\n",
    "* `world_mean.loc[:,'inflation_mean'] >= 50`を使い，列`inflation_mean`の要素が50以上であれば`True`，50未満であれば`False`となる`Series`を返す。\n",
    "* `True`は１，`False`は０と等しいので，`.sum()`で合計することによってハイパーインフレ国の数が計算できる。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "短期的なノイズの影響によってある年にハイパーインフレが発生する場合もあるだろう。しかしこの結果は，長期的にハイパーインフレに悩まされる国が存在することを示している。どのような国なのかを確認するために，インフレ率上位10ヵ国を表示してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>income_group</th>\n",
       "      <th>money_growth_mean</th>\n",
       "      <th>inflation_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iso</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COD</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>Low income</td>\n",
       "      <td>122.442962</td>\n",
       "      <td>127.704505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGO</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>108.457354</td>\n",
       "      <td>92.231798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRA</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>92.023344</td>\n",
       "      <td>88.882771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>79.169900</td>\n",
       "      <td>74.971614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NIC</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>67.774841</td>\n",
       "      <td>62.717663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLR</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>65.667637</td>\n",
       "      <td>62.536094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UKR</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>58.537270</td>\n",
       "      <td>61.867179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER</th>\n",
       "      <td>Peru</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>54.905117</td>\n",
       "      <td>48.305760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARM</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>48.970412</td>\n",
       "      <td>46.680070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZE</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>52.949213</td>\n",
       "      <td>45.247656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country         income_group  money_growth_mean  inflation_mean\n",
       "iso                                                                          \n",
       "COD  Congo, Dem. Rep.           Low income         122.442962      127.704505\n",
       "AGO            Angola  Lower middle income         108.457354       92.231798\n",
       "BRA            Brazil  Upper middle income          92.023344       88.882771\n",
       "ARG         Argentina  Upper middle income          79.169900       74.971614\n",
       "NIC         Nicaragua  Lower middle income          67.774841       62.717663\n",
       "BLR           Belarus  Upper middle income          65.667637       62.536094\n",
       "UKR           Ukraine  Lower middle income          58.537270       61.867179\n",
       "PER              Peru  Upper middle income          54.905117       48.305760\n",
       "ARM           Armenia  Upper middle income          48.970412       46.680070\n",
       "AZE        Azerbaijan  Upper middle income          52.949213       45.247656"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_mean.sort_values(by='inflation_mean', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "やはり所得水準が比較的に低い国が入っている。\n",
    "\n",
    "`world_mean`を使いクロスセクションのデータをプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_ = world_mean.plot.scatter(x='money_growth_mean', y='inflation_mean')\n",
    "xpoints = ypoints = ax_.get_ylim()\n",
    "ax_.plot(xpoints,ypoints,'r-', label='45度線')\n",
    "ax_.set_title('平均インフレ率とマネーストトックの平均成長率', size='15')\n",
    "ax_.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "綺麗に45度線上に並んでいる。国ごとに平均を計算することによって短期的なノイズが相殺され長期的な関係が浮かび上がっている。トレンド線の傾きを計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "標本の大きさ：177\n",
      "調整済み決定係数：0.945\n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept            -6.0805      0.461    -13.175      0.000      -6.991      -5.170\n",
      "money_growth_mean     0.9847      0.018     54.876      0.000       0.949       1.020\n",
      "=====================================================================================\n"
     ]
    }
   ],
   "source": [
    "res_world_mean = smf.ols('inflation_mean ~ money_growth_mean', data=world_mean).fit()\n",
    "print(f'標本の大きさ：{int(res_world_mean.nobs)}')\n",
    "print(f'調整済み決定係数：{res_world_mean.rsquared_adj:.3f}')\n",
    "print(res_world_mean.summary().tables[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "推定値は１に非常に近い。もし図の左下にある外れ値のように見える値（ジンバブエ）を省くとスロープ係数は0.997になる。長期的には式[](eq:11-qtm_growth_long)が示すように，マネーストック増加率の1％上昇はインフレ率1％上昇につかがること示す結果である。「真のメカニズム」の一部が垣間見えるような気がしませんか。"
   ]
  }
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