From 822c07a3680150de5836d5fd0871495d38030c0d Mon Sep 17 00:00:00 2001
From: Tetsu Haruyama <tetsu.yes@gmail.com>
Date: Sat, 10 Aug 2024 13:18:45 +0900
Subject: [PATCH] x y in plot + 1.21 legacy

---
 13_random_TFP.ipynb    | 63 ++++++++++++++++++-----------
 14_linearsolve.ipynb   |  2 +-
 15_RBC.ipynb           | 90 ++++++++++++++++++++++++++----------------
 16_data_on_money.ipynb | 25 +++++++-----
 4 files changed, 113 insertions(+), 67 deletions(-)

diff --git a/13_random_TFP.ipynb b/13_random_TFP.ipynb
index 36d329e0..13e7a21f 100644
--- a/13_random_TFP.ipynb
+++ b/13_random_TFP.ipynb
@@ -29,7 +29,7 @@
     "import statsmodels.formula.api as smf\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\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\")"
@@ -37,7 +37,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "## はじめに"
    ]
@@ -101,7 +103,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### ホワイト・ノイズ"
@@ -180,7 +183,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 図示"
@@ -279,7 +283,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 自己共分散（自己相関）"
@@ -430,7 +435,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 自己回帰モデル：AR(1)"
@@ -440,7 +446,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 説明"
@@ -470,7 +477,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### ３つの例：持続性の違い"
@@ -622,7 +630,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 説明"
@@ -647,7 +656,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### データ"
@@ -696,7 +706,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 労働時間と雇用の特徴"
@@ -757,7 +768,7 @@
    },
    "outputs": [],
    "source": [
-    "df[['hours','hours_trend']].plot()\n",
+    "df.plot(y=['hours','hours_trend'])\n",
     "pass"
    ]
   },
@@ -778,7 +789,7 @@
    },
    "outputs": [],
    "source": [
-    "df[['employed','employed_trend']].plot()\n",
+    "df.plot(y=['employed','employed_trend'])\n",
     "pass"
    ]
   },
@@ -799,7 +810,7 @@
    },
    "outputs": [],
    "source": [
-    "df[['total_hours','total_hours_trend']].plot()\n",
+    "df.plot(y=['total_hours','total_hours_trend'])\n",
     "pass"
    ]
   },
@@ -822,7 +833,7 @@
    },
    "outputs": [],
    "source": [
-    "ax_ = df[['hours_cycle','employed_cycle']].plot()\n",
+    "ax_ = df.plot(y=['hours_cycle','employed_cycle'])\n",
     "ax_.axhline(0, c='red', lw=0.5)\n",
     "pass"
    ]
@@ -911,7 +922,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### TFPの特徴"
@@ -955,7 +967,7 @@
     "df['tfp_cycle'] = df['tfp_log'] - df['tfp_log_trend']\n",
     "\n",
     "# 全要素生産性とトレンドのプロット\n",
-    "ax = df[['tfp_log','tfp_log_trend']].plot()\n",
+    "ax = df.plot(y=['tfp_log','tfp_log_trend'])\n",
     "ax.set_title('全要素生産性とトレンド（対数）', size=20)\n",
     "pass"
    ]
@@ -977,7 +989,7 @@
    },
    "outputs": [],
    "source": [
-    "ax = (100 * df['tfp_cycle']).plot()\n",
+    "ax = ( 100 * df['tfp_cycle'] ).plot()\n",
     "ax.axhline(0, c='red')\n",
     "ax.set_title('全要素生産性の変動（％）', size=20)\n",
     "pass"
@@ -1022,7 +1034,7 @@
    },
    "outputs": [],
    "source": [
-    "ax_ = (100 * df[['gdp_cycle','tfp_cycle']]).plot()\n",
+    "ax_ = ( 100 * df[['gdp_cycle','tfp_cycle']] ).plot()\n",
     "ax_.set_title('GDPとTFPの変動（％）',size=20)\n",
     "pass"
    ]
@@ -1102,7 +1114,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### AR(1)としてのTFP"
@@ -1296,8 +1309,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true,
-    "jp-MarkdownHeadingCollapsed": true
+    "heading_collapsed": true
    },
    "source": [
     "### 説明"
@@ -1592,7 +1604,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s = (df['investment']/df['gdp']).mean()\n",
+    "s = ( df['investment']/df['gdp'] ).mean()\n",
     "s"
    ]
   },
@@ -1781,7 +1793,7 @@
    },
    "outputs": [],
    "source": [
-    "np.log(df_sim['Y']).plot(title='シミュレーション：Yとトレンド（対数）')\n",
+    "np.log( df_sim['Y'] ).plot(title='シミュレーション：Yとトレンド（対数）')\n",
     "df_sim['Y_log_trend'].plot()\n",
     "pass"
    ]
@@ -1857,6 +1869,7 @@
     "print('\\n--- シミュレーション：変動の自己相関係数 ---\\n')\n",
     "\n",
     "for v in var_list:\n",
+    "    \n",
     "    ac = df_sim[f'{v}の変動'].autocorr()\n",
     "    print(f'{v}の変動： {ac:.3f}')"
    ]
@@ -1892,6 +1905,7 @@
     "data_var_list = ['consumption','investment','capital']\n",
     "\n",
     "for v in data_var_list:\n",
+    "    \n",
     "    df[f'{v}_cycle'] = np.log( df[v]/py4macro.trend(df[v]) )"
    ]
   },
@@ -1926,6 +1940,7 @@
     "print('\\n--- データ：変動の自己相関係数 ---\\n')\n",
     "\n",
     "for v in data_var_list:\n",
+    "    \n",
     "    ac = df[f'{v}_cycle'].autocorr()\n",
     "    print(f'{v:>11}の変動： {ac:>5.3f}')      # 1"
    ]
diff --git a/14_linearsolve.ipynb b/14_linearsolve.ipynb
index 84b4af98..fb4ed658 100644
--- a/14_linearsolve.ipynb
+++ b/14_linearsolve.ipynb
@@ -27,7 +27,7 @@
     "import pandas as pd\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\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\")"
diff --git a/15_RBC.ipynb b/15_RBC.ipynb
index 310f5d9b..d59dc50c 100644
--- a/15_RBC.ipynb
+++ b/15_RBC.ipynb
@@ -28,7 +28,7 @@
     "import pandas as pd\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\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\")"
@@ -37,7 +37,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 説明"
@@ -73,7 +74,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "## 基本RBCモデルの詳細"
    ]
@@ -174,7 +177,7 @@
     "U_0=u_0+\\beta u_1+\\beta^2 u_2+\\beta^3 u_3+\\cdots\n",
     "$$ (eq:15-lifetimeU)\n",
     "\n",
-    "ここで$0<\\beta<1$（例えば、0.99）は主観的割引因子と呼ばれるものであり、将来の効用の重要性（ウェイト）を捉えている。注意する点は，$\\beta^t>\\beta^{t+1}$が必ず成立するため，消費者にとって将来の効用の重要性は減少するということである。将来も重要だが，将来よりも今の方が大事だという考えを捉えている。$\\beta$の意味をもう少し説明するために$\\beta=1$としてみよう。この場合、50年後や100年後の将来の効用は今の効用と等しく重要だという意味であり，そのような消費者は極端に我慢強いと言える。そういう意味で、高い$\\beta$はより我慢強い選好を表している。\n",
+    "ここで$0<\\beta<1$（例えば、0.99）は主観的割引因子と呼ばれるものであり、将来の効用の重要性（ウェイト）を捉えている。まず、$\\beta=0$を考えてみよう。この場合、将来の効用は`0`であり、将来は全く重要ではなく、今期だけが重要だということになる。次に、$\\beta=1$としてみよう。この場合、50年後や100年後の将来の効用は今の効用と等しく重要だという意味であり，そのような消費者は極端に我慢強いと言える。そういう意味で、高い$\\beta$はより我慢強い選好を表している。通常は$0<\\beta<1$を仮定し、$\\beta^t>\\beta^{t+1}$が必ず成立する。即ち，消費者にとって将来の効用の重要性は減少するということである。将来も重要だが，将来よりも今の方が大事だという考えを捉えている。\n",
     "\n",
     "無限期間生きる消費者の効用最大化問題は非常に難しいと思うかもしれない。実際，数学的に緻密な計算をするのは高度な数学が求められる。しかし，ミクロ経済学で学んだ単純な消費者の効用最大化問題を理解することができれば，簡単に理解できるものとなっている。そのために，まず２財の効用最大化問題を復習してみよう。"
    ]
@@ -1148,6 +1151,7 @@
     "var_list = ['y','c','i']\n",
     "\n",
     "for v in var_list:\n",
+    "    \n",
     "    var = rbc_basic_model.simulated[v].std() / rbc_basic_model.simulated['y'].std()\n",
     "    print(f'{v}  {var:.2f}')"
    ]
@@ -1316,7 +1320,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "## 内生的労働供給の導入"
    ]
@@ -1324,7 +1330,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 消費者の最適化行動"
@@ -1385,7 +1392,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 均衡条件"
@@ -1443,7 +1451,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### シミュレーション"
@@ -1601,21 +1610,21 @@
     "    p = parameters\n",
     "\n",
     "    # 均衡条件\n",
-    "    euler = p['beta']*( 1+fwd['r'])/fwd['c'] - 1/cur['c']\n",
+    "    euler = p['beta'] * ( 1+fwd['r']) / fwd['c'] - 1/cur['c']\n",
     "    \n",
-    "    labour_supply = p['m']/( (1-cur['l'])*cur['w'] ) - 1/cur['c']\n",
+    "    labour_supply = p['m'] / ( (1-cur['l'])*cur['w'] ) - 1/cur['c']\n",
     "\n",
-    "    capital_change = cur['i'] + (1-p['d'])*cur['k'] - fwd['k']\n",
+    "    capital_change = cur['i'] + (1-p['d']) * cur['k'] - fwd['k']\n",
     "    \n",
-    "    production_function = cur['a']*cur['k']**p['alpha']*cur['l']**(1-p['alpha']) - cur['y']   # (5)    \n",
+    "    production_function = cur['a'] * cur['k']**p['alpha'] * cur['l']**(1-p['alpha']) - cur['y']   # (5)    \n",
     "    \n",
-    "    income_id = cur['c']+cur['i']-cur['y']\n",
+    "    income_id = cur['c'] + cur['i'] - cur['y']\n",
     "    \n",
-    "    wage = (1-p['alpha'])*cur['y']/cur['l'] - cur['w']\n",
+    "    wage = ( 1-p['alpha'] ) * cur['y'] / cur['l'] - cur['w']\n",
     "    \n",
-    "    interest_rate = p['alpha']*cur['y']/cur['k']-p['d']-cur['r']\n",
+    "    interest_rate = p['alpha'] * cur['y'] / cur['k'] - p['d'] - cur['r']\n",
     "    \n",
-    "    tfp = cur['a']**p['rho']-fwd['a']\n",
+    "    tfp = cur['a']**p['rho'] - fwd['a']\n",
     "    \n",
     "    # 返り値\n",
     "    return np.array([euler,\n",
@@ -1710,7 +1719,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### インパクト効果"
@@ -1759,7 +1769,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### インパルス反応"
@@ -1865,7 +1876,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "### コードを１つのセルにまとめる"
    ]
@@ -1903,21 +1916,21 @@
     "    p = parameters\n",
     "\n",
     "    # 均衡条件\n",
-    "    euler = p['beta']*( 1+fwd['r'])/fwd['c'] - 1/cur['c']\n",
+    "    euler = p['beta'] * ( 1+fwd['r'] ) / fwd['c'] - 1/cur['c']\n",
     "    \n",
-    "    labour_supply = p['m']/( (1-cur['l'])*cur['w'] ) - 1/cur['c']\n",
+    "    labour_supply = p['m'] / ( (1-cur['l'])*cur['w'] ) - 1/cur['c']\n",
     "\n",
-    "    capital_change = cur['i'] + (1-p['d'])*cur['k'] - fwd['k']\n",
+    "    capital_change = cur['i'] + (1-p['d']) * cur['k'] - fwd['k']\n",
     "    \n",
-    "    production_function = cur['a']*cur['k']**p['alpha']*cur['l']**(1-p['alpha']) - cur['y']   # (5)    \n",
+    "    production_function = cur['a']  *cur['k']**p['alpha'] * cur['l']**(1-p['alpha']) - cur['y']   # (5)    \n",
     "    \n",
-    "    income_id = cur['c']+cur['i']-cur['y']\n",
+    "    income_id = cur['c']+cur['i'] - cur['y']\n",
     "    \n",
-    "    wage = (1-p['alpha'])*cur['y']/cur['l'] - cur['w']\n",
+    "    wage = (1-p['alpha']) * cur['y'] / cur['l'] - cur['w']\n",
     "    \n",
-    "    interest_rate = p['alpha']*cur['y']/cur['k']-p['d']-cur['r']\n",
+    "    interest_rate = p['alpha'] * cur['y'] / cur['k'] - p['d'] - cur['r']\n",
     "    \n",
-    "    tfp = cur['a']**p['rho']-fwd['a']\n",
+    "    tfp = cur['a']**p['rho'] - fwd['a']\n",
     "    \n",
     "    # 返り値\n",
     "    return np.array([euler,\n",
@@ -1940,13 +1953,16 @@
     "# 定常状態の計算\n",
     "rbc_model.compute_ss([1, 10, 1, 2, 0.1, 1, 1, 0.5])\n",
     "print('定常値')\n",
+    "\n",
     "for i in range(len(rbc_model.ss)):\n",
     "    print(f'{rbc_model.ss.index[i]}: {rbc_model.ss.iloc[i]:.3f}')\n",
+    "    \n",
     "print('')\n",
     "\n",
     "# 対数線形近似\n",
     "rbc_model.approximate_and_solve()\n",
     "print('対数線形近似')\n",
+    "\n",
     "for s in rbc_model.solved().split('\\n\\n')[1:]:\n",
     "    print(s, sep='')\n",
     "\n",
@@ -1967,7 +1983,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 確率的シミュレーション"
@@ -2019,7 +2036,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### TFPと産出量"
@@ -2059,7 +2077,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 変動の大きさ"
@@ -2113,7 +2132,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### 自己相関"
@@ -2130,6 +2150,7 @@
     "print('　内生的労働供給\\t基本モデル')\n",
     "\n",
     "for v in var_list:\n",
+    "    \n",
     "    autocorr = rbc_model.simulated[v].autocorr()\n",
     "    autocorr_basic = rbc_basic_model.simulated[v].autocorr()\n",
     "    print(f'{v}\\t{autocorr:.2f}\\t{autocorr_basic:.2f}')"
@@ -2155,7 +2176,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "#### GDPとの相関度"
@@ -2172,6 +2194,7 @@
     "print('　内生的労働供給\\t基本モデル')\n",
     "\n",
     "for v in var_list:\n",
+    "    \n",
     "    corr = rbc_model.simulated[['y', v]].corr().iloc[0,1]\n",
     "    corr_basic = rbc_basic_model.simulated[['y', v]].corr().iloc[0,1]\n",
     "    print(f'{v}\\t{corr:.2f}\\t{corr_basic:.2f}')"
@@ -2196,7 +2219,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## RBCモデルの意義と限界"
diff --git a/16_data_on_money.ipynb b/16_data_on_money.ipynb
index 0d363c8d..e3824b51 100644
--- a/16_data_on_money.ipynb
+++ b/16_data_on_money.ipynb
@@ -29,7 +29,7 @@
     "import py4macro\n",
     "\n",
     "# numpy v1の表示を使用\n",
-    "np.set_printoptions(legacy='1.25')\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\")"
@@ -38,7 +38,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## はじめに"
@@ -62,7 +63,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## GDPとインフレ率"
@@ -110,7 +112,7 @@
     "df['GDPの変動'] = 100*np.log( df['gdp']/py4macro.trend(df['gdp']) )\n",
     "df['インフレ率の変動'] = df['inflation'] - py4macro.trend(df['inflation'])\n",
     "\n",
-    "ax_ = df[['GDPの変動','インフレ率の変動']].plot()\n",
+    "ax_ = df.plot(y=['GDPの変動','インフレ率の変動'])\n",
     "ax_.set_title('トレンドからの乖離（％）', size=20)\n",
     "pass"
    ]
@@ -168,7 +170,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "heading_collapsed": true
+    "heading_collapsed": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## 金融政策"
@@ -187,7 +190,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 相対価格（おさらい）"
@@ -263,7 +267,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### ブレトンウッズ体制"
@@ -290,7 +295,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### データ"
@@ -468,7 +474,8 @@
    "cell_type": "markdown",
    "metadata": {
     "heading_collapsed": true,
-    "hidden": true
+    "hidden": true,
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "### 結果の要因"