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SARIMAX - Stata Examples
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stata ARIMA Examples\n",
"\n",
"See http://www.stata.com/manuals13/tsarima.pdf Examples 1-4\n",
"\n",
"and http://www.stata.com/manuals13/tsarimapostestimation.pdf Example 1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 1: Arima"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('wpi1.dta')\n",
"data.index = data.t\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: D.wpi No. Observations: 124\n",
"Model: SARIMAX(1, 1, 1) Log Likelihood -134.983\n",
"Date: Wed, 13 Aug 2014 AIC 277.965\n",
"Time: 01:01:03 BIC 289.246\n",
"Sample: 01-01-1960 HQIC 282.548\n",
" - 10-01-1990 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"intercept 0.1050 0.057 1.837 0.066 -0.007 0.217\n",
"ar.L1 0.8740 0.061 14.215 0.000 0.753 0.994\n",
"ma.L1 -0.4206 0.120 -3.508 0.000 -0.656 -0.186\n",
"sigma2 0.5226 0.067 7.840 0.000 0.392 0.653\n",
"==============================================================================\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 2: Arima with additive seasonal effects"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('wpi1.dta')\n",
"data.index = data.t\n",
"data['ln_wpi'] = np.log(data['wpi'])\n",
"data['D.ln_wpi'] = data['ln_wpi'].diff()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Graph data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,4))\n",
"\n",
"# Levels\n",
"axes[0].plot(data.index, data['wpi'])\n",
"axes[0].set(title='US Wholesale Price Index')\n",
"\n",
"# Log difference\n",
"axes[1].plot(data.index, data['D.ln_wpi'])\n",
"axes[1].hlines(0, data.index[0], data.index[-1], 'r')\n",
"axes[1].set(title='US Wholesale Price Index - difference of logs');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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gpM6VwIWR7/6xm22up9/6oszYoAG6oyPv/X64LJFnAq8AdvQ8b6LW+t2e550L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\nlZE5bJOBv9U4dLJlQFo+2i3oxrtTUj5bs3eaGXNcCTIey9JaxloHSC0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ho1ENkee+7Pjh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\n0BwxREciqaIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109b6b410>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Graph data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,4))\n",
"\n",
"fig = sm.graphics.tsa.plot_acf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[0])\n",
"fig = sm.graphics.tsa.plot_pacf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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OgWsAf966+8T27t7xew7Fj48dTRy3s7tv0NsHehLuRYtfOAl44d2bW9Y1X3/P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\nqE6wAUdERERERFQn2IAjIiIiIiKqE/8/yzgs6zk03vcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109f5b990>"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Fit the model\n",
"mod = sm.tsa.SARIMAX(data['ln_wpi'], trend='c', order=(1,1,(1,0,0,1)))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"=================================================================================\n",
"Dep. Variable: D.ln_wpi No. Observations: 124\n",
"Model: SARIMAX(1, 1, (1, 4)) Log Likelihood 386.151\n",
"Date: Wed, 13 Aug 2014 AIC -762.301\n",
"Time: 00:59:19 BIC -748.200\n",
"Sample: 01-01-1960 HQIC -756.573\n",
" - 10-01-1990 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"intercept 0.0025 0.001 2.063 0.039 0.000 0.005\n",
"ar.L1 0.7837 0.081 9.730 0.000 0.626 0.942\n",
"ma.L1 -0.4044 0.105 -3.855 0.000 -0.610 -0.199\n",
"ma.L4 0.3015 0.105 2.861 0.004 0.095 0.508\n",
"sigma2 0.0001 1.38e-05 7.856 0.000 8.16e-05 0.000\n",
"==============================================================================\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 3: Airline Model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('air2.dta')\n",
"data.index = pd.date_range(start=datetime(data.time[0], 1, 1), periods=len(data), freq='MS')\n",
"data['lnair'] = np.log(data['air'])\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.SARIMAX(data['lnair'], order=(0,1,1), seasonal_order=(0,1,1,12))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==========================================================================================\n",
"Dep. Variable: D.DS12.lnair No. Observations: 144\n",
"Model: SARIMAX(0, 1, 1)x(0, 1, 1, 12) Log Likelihood 244.696\n",
"Date: Wed, 13 Aug 2014 AIC -483.393\n",
"Time: 01:35:13 BIC -474.484\n",
"Sample: 01-01-1949 HQIC -479.773\n",
" - 12-01-1960 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"ma.L1 -0.4017 0.090 -4.448 0.000 -0.579 -0.225\n",
"ma.S.L12 -0.5569 0.074 -7.551 0.000 -0.701 -0.412\n",
"sigma2 0.0013 0.000 8.070 0.000 0.001 0.002\n",
"==============================================================================\n"
]
}
],
"prompt_number": 90
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Example 4: ARMAX (Friedman)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"data = pd.read_stata('friedman2.dta')\n",
"data.index = data.time\n",
"\n",
"# Variables\n",
"endog = data.ix['1959':'1981', 'consump']\n",
"exog = sm.add_constant(data.ix['1959':'1981', 'm2'])\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.SARIMAX(endog, exog, order=(1,0,1))\n",
"res = mod.fit()\n",
"print res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: consump No. Observations: 92\n",
"Model: SARIMAX(1, 0, 1) Log Likelihood -340.508\n",
"Date: Wed, 13 Aug 2014 AIC 691.015\n",
"Time: 00:47:41 BIC 703.624\n",
"Sample: 01-01-1959 HQIC 696.105\n",
" - 10-01-1981 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const -36.0584 34.019 -1.060 0.289 -102.734 30.617\n",
"x1 1.1220 0.032 34.567 0.000 1.058 1.186\n",
"ar.L1 0.9348 0.040 23.369 0.000 0.856 1.013\n",
"ma.L1 0.3091 0.112 2.762 0.006 0.090 0.528\n",
"sigma2 93.2554 13.755 6.780 0.000 66.296 120.215\n",
"==============================================================================\n"
]
}
],
"prompt_number": 133
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ARIMA Postestimation: Example 1 - Dynamic Forecasting"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dataset\n",
"raw = pd.read_stata('friedman2.dta')\n",
"raw.index = raw.time\n",
"data = raw.ix[:'1981']\n",
"\n",
"# Variables\n",
"endog = data.ix['1959':, 'consump']\n",
"exog = sm.add_constant(data.ix['1959':, 'm2'])\n",
"nobs = endog.shape[0]\n",
"\n",
"# Fit the model\n",
"mod = sm.tsa.SARIMAX(endog.ix[:'1978-01-01'], exog=exog.ix[:'1978-01-01'], order=(1,0,1))\n",
"fit_res = mod.fit()\n",
"print fit_res.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: consump No. Observations: 77\n",
"Model: SARIMAX(1, 0, 1) Log Likelihood -243.316\n",
"Date: Wed, 13 Aug 2014 AIC 496.633\n",
"Time: 01:18:26 BIC 508.352\n",
"Sample: 01-01-1959 HQIC 501.320\n",
" - 01-01-1978 \n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 0.6745 14.273 0.047 0.962 -27.299 28.648\n",
"x1 1.0379 0.019 54.687 0.000 1.001 1.075\n",
"ar.L1 0.8775 0.061 14.294 0.000 0.757 0.998\n",
"ma.L1 0.2771 0.100 2.768 0.006 0.081 0.473\n",
"sigma2 31.6982 5.110 6.203 0.000 21.683 41.714\n",
"==============================================================================\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Prediction\n",
"npredict = data.ix['1978-01-01':].shape[0]\n",
"mod = sm.tsa.SARIMAX(endog, exog=exog, order=(1,0,1))\n",
"mod.update(fit_res.params)\n",
"res = mod.filter()\n",
"\n",
"# Graph\n",
"fig, ax = plt.subplots(figsize=(9,4))\n",
"npre = 4\n",
"ax.set(title='Personal consumption', xlabel='Date', ylabel='Billions of dollars')\n",
"ax.plot(data.index[-npredict-npre+1:], data.ix[-npredict-npre+1:, 'consump'], 'o', label='Observed')\n",
"\n",
"# In-sample one-step-ahead predictions and 95% confidence intervals (forecast without data)\n",
"predict, cov, ci, idx = res.predict(alpha=0.05)\n",
"ax.plot(idx[-npredict-npre:], predict[0, -npredict-npre:], 'r--', label='One-step-ahead forecast');\n",
"ax.plot(idx[-npredict-npre:], ci[0, -npredict-npre:], 'r--', alpha=0.3)\n",
"\n",
"# Dynamic predictions and 95% confidence intervals\n",
"predict_dy, cov_dy, ci_dy, idx_dy = res.predict(dynamic=nobs-npredict-1, alpha=0.01)\n",
"ax.plot(idx_dy[-npredict-npre:], predict_dy[0, -npredict-npre:], 'g', label='Dynamic forecast (1978)');\n",
"ax.plot(idx_dy[-npredict-npre:], ci_dy[0, -npredict-npre:], 'g:', alpha=0.3);\n",
"\n",
"legend = ax.legend(loc='lower right')\n",
"legend.get_frame().set_facecolor('w')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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SA5yCyHuAdxIcrv8A/KXI0GOBV4F/V+O2WWd0Sxw55yYCU4FbvPfVkSCpm1TTPnUOsykd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\nFAqFE0o5UigUCoVCoXDi/wNf6qmdo94dgwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b7c1050>"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Prediction error\n",
"\n",
"# Graph\n",
"fig, ax = plt.subplots(figsize=(9,4))\n",
"npre = 4\n",
"ax.set(title='Forecast error', xlabel='Date', ylabel='Forecast - Actual')\n",
"\n",
"# In-sample one-step-ahead predictions and 95% confidence intervals (forecast without data)\n",
"predict_error = predict[0, -npredict-1:] - endog.iloc[-npredict-1:]\n",
"predict_ci = ci[0, -npredict-1:] - endog.iloc[-npredict-1:][:, None]\n",
"ax.plot(idx[-npredict-1:], predict_error, label='One-step-ahead forecast');\n",
"ax.plot(idx[-npredict-1:], predict_ci, 'b--', alpha=0.4)\n",
"\n",
"# Dynamic predictions and 95% confidence intervals\n",
"predict_dy_error = predict_dy[0, -npredict-1:] - endog.iloc[-npredict-1:]\n",
"predict_dy_ci = ci_dy[0, -npredict-1:] - endog.iloc[-npredict-1:][:, None]\n",
"ax.plot(idx[-npredict-1:], predict_dy_error, 'r', label='Dynamic forecast (1978)');\n",
"ax.plot(idx[-npredict-1:], predict_dy_ci, 'r--', alpha=0.4)\n",
"\n",
"legend = ax.legend(loc='lower left');\n",
"legend.get_frame().set_facecolor('w')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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ZpwIvNMPFVOvfu3uYHI0pJP2w5Ihp9VAA3Xe8NmYJyilCm2DhHY/mLY8X/g4j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\nkyYxfPhwWrZsyZgxY5gwYUKF3gtFUZTaRo0f5Y8k8XiUvzbRuHFjsrO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"text": [
"<matplotlib.figure.Figure at 0x109e0d290>"
]
}
],
"prompt_number": 89
}
],
"metadata": {}
}
]
}
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