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spring-haruspring-haru
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iloc[0], etc
1 parent 3f2dea0 commit 4585b7a

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‎11b_Money_Inflation.ipynb‎

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},
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{
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"cell_type": "markdown",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"metadata": {},
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"source": [
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"## 日本の時系列データ"
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]
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"jp-MarkdownHeadingCollapsed": true
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"heading_collapsed": true
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},
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"source": [
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"### 散布図とトレンド線"
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" df.sort_values('トレンド').plot(x='money_growth', y='トレンド', #5\n",
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" color='r', ax=ax_) #6\n",
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" ax_.set_title(f'{t}\\n' #7\n",
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" f'スロープ係数：{res.params[1]:.3f}\\n' #8\n",
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" f'p値：{res.pvalues[1]:.3f}\\n' #9\n",
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" f'スロープ係数：{res.params.iloc[1]:.3f}\\n' #8\n",
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" f'p値：{res.pvalues.iloc[1]:.3f}\\n' #9\n",
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" f'調整済み決定係数：{res.rsquared_adj:.3f}', #10\n",
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" size=18, loc='left') #11"
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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"version": "3.13.11"
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}
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},
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"nbformat": 4,

‎15_RBC.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"jp-MarkdownHeadingCollapsed": true
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"heading_collapsed": true
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},
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"source": [
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"### 実質利子率"
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"jp-MarkdownHeadingCollapsed": true
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"heading_collapsed": true
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},
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"source": [
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"### シミュレーション"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true
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},
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"outputs": [],
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"execution_count": 2,
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"metadata": {
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"hidden": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"alpha 0.360000\n",
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"beta 0.990000\n",
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"d 0.025000\n",
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"rho 0.551000\n",
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"sigma 0.000061\n",
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"theta 1.000000\n",
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"L 0.330000\n",
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"dtype: float64"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"parameters = pd.Series({'alpha':.36,\n",
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" 'beta':0.99,\n",
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"hidden": true
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},
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{
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"cell_type": "code",
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"metadata": {
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"hidden": true
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"hidden": true
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},
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"hidden": true
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},
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"hidden": true
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},
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"cell_type": "code",
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"metadata": {
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"hidden": true
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"outputs": [],
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"execution_count": 8,
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"metadata": {
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"hidden": true
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"outputs": [
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{
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"data": {
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"text/plain": [
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"a 1.000000\n",
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"k 12.536454\n",
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"y 1.222339\n",
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"c 0.908928\n",
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"i 0.313411\n",
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"r 0.010101\n",
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"w 2.370598\n",
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"dtype: float64"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rbc_basic_model.ss"
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]
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},
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"cell_type": "code",
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"outputs": [],
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"execution_count": 9,
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"metadata": {
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"hidden": true
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},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "'Series' object has no attribute 'ravel'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
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"\u001b[32m/var/folders/_f/g_05tt3j09n5hcrk1mwhyj5r0000gn/T/ipykernel_42398/3130218754.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m rbc_basic_model.approximate_and_solve(log_linear=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
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"\u001b[32m~/miniforge3/envs/jb1/lib/python3.11/site-packages/linearsolve/__init__.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, log_linear, eigenvalue_warnings)\u001b[39m\n\u001b[32m 264\u001b[39m self.log_linear = log_linear\n\u001b[32m 265\u001b[39m \n\u001b[32m 266\u001b[39m \u001b[38;5;66;03m# Approximate\u001b[39;00m\n\u001b[32m 267\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m log_linear == \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m268\u001b[39m self.log_linear_approximation()\n\u001b[32m 269\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 270\u001b[39m self.linear_approximation()\n\u001b[32m 271\u001b[39m \n",
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"\u001b[32m~/miniforge3/envs/jb1/lib/python3.11/site-packages/linearsolve/__init__.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, steady_state)\u001b[39m\n\u001b[32m 698\u001b[39m \n\u001b[32m 699\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m np.iscomplexobj(self.parameters):\n\u001b[32m 700\u001b[39m \n\u001b[32m 701\u001b[39m \u001b[38;5;66;03m# Assign attributes\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m702\u001b[39m self.a= approx_fprime_cs(np.log(self.ss).ravel(),log_equilibrium_fwd)\n\u001b[32m 703\u001b[39m self.b= -approx_fprime_cs(np.log(self.ss).ravel(),log_equilibrium_cur)\n\u001b[32m 704\u001b[39m \n\u001b[32m 705\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
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"\u001b[32m~/miniforge3/envs/jb1/lib/python3.11/site-packages/pandas/core/generic.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 6202\u001b[39m \u001b[38;5;28;01mand\u001b[39;00m name \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m self._accessors\n\u001b[32m 6203\u001b[39m \u001b[38;5;28;01mand\u001b[39;00m self._info_axis._can_hold_identifiers_and_holds_name(name)\n\u001b[32m 6204\u001b[39m ):\n\u001b[32m 6205\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m self[name]\n\u001b[32m-> \u001b[39m\u001b[32m6206\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m object.__getattribute__(self, name)\n",
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"\u001b[31mAttributeError\u001b[39m: 'Series' object has no attribute 'ravel'"
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]
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}
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],
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"source": [
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"rbc_basic_model.approximate_and_solve(log_linear=True)"
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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"version": "3.13.11"
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}
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},
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"nbformat": 4,

‎5a_Development_Accounting.ipynb‎

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‎6_Regression.ipynb‎

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