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Created August 23, 2016 18:15
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GSoC 2016: VECM
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing Modules and Loading the Data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas\n",
"from statsmodels.tsa.vecm.vecm import VECM, select_order\n",
"import data as dta"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iidata = dta.load_pandas();\n",
"mdata = iidata.data\n",
"dates = mdata[['year', 'quarter']].astype(int).astype(str)\n",
"quarterly = dates[\"year\"] + \"Q\" + dates[\"quarter\"]\n",
"from statsmodels.tsa.base.datetools import dates_from_str\n",
"quarterly = dates_from_str(quarterly)\n",
"mdata = mdata[dta.variable_names]\n",
"mdata.index = pandas.DatetimeIndex(quarterly)\n",
"data = mdata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using the VECM Framework without Deterministic Terms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fitting the VECM to the Data (Parameter Estimation)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/vegcev/.local/lib/python3.4/site-packages/numpy/lib/shape_base.py:431: FutureWarning: in the future np.array_split will retain the shape of arrays with a zero size, instead of replacing them by `array([])`, which always has a shape of (0,).\n",
" FutureWarning)\n"
]
}
],
"source": [
"model = VECM(data, diff_lags=3, coint_rank=1)\n",
"vecm_res = model.fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The estimated parameters are now stored in the variable `vecm_res` (an instance of `VECMResults`) together with the original data. In this example quarterly inflation (`Dp`) and interest (`R`) data is used. The data can be plotted via `plot_data`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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HSTLPaQjSddJJvGTGlIrgjDkyQsRLlK4C2LqVUoumbs/rXuendKWlF4sqXRMT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fet2fa3v8GQIV4H6eSTfRDzc8/V/9pSfObP9xb12tYwLQedO/sHidmz6z5WKlvUSbxzgQ4pjzvg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7f52b6438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vecm_res.plot_data(with_presample=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The VECM-parameters estimated from the data can be accessed in code..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.5877, 0.1173, -0.7036, 0.1875, -0.8253, 0.0231],\n",
" [-0.0823, 0.186 , -0.0403, -0.0447, 0.0091, 0.1722]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.gamma.round(4)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.54600703],\n",
" [ 0.10574224]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.alpha"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. ],\n",
" [-0.11056362]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.beta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... or printed via `VECMResult`'s `summary`-method."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Det. terms outside coint. relation & lagged endog. parameters for equation Dp</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>L1.Dp</th> <td> -0.5877</td> <td> 0.144</td> <td> -4.072</td> <td> 0.000</td> <td> -0.871</td> <td> -0.305</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L1.R</th> <td> 0.1173</td> <td> 0.112</td> <td> 1.046</td> <td> 0.296</td> <td> -0.103</td> <td> 0.337</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.Dp</th> <td> -0.7036</td> <td> 0.099</td> <td> -7.124</td> <td> 0.000</td> <td> -0.897</td> <td> -0.510</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.R</th> <td> 0.1875</td> <td> 0.113</td> <td> 1.653</td> <td> 0.098</td> <td> -0.035</td> <td> 0.410</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.Dp</th> <td> -0.8253</td> <td> 0.053</td> <td> -15.489</td> <td> 0.000</td> <td> -0.930</td> <td> -0.721</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.R</th> <td> 0.0231</td> <td> 0.112</td> <td> 0.207</td> <td> 0.836</td> <td> -0.196</td> <td> 0.242</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Det. terms outside coint. relation & lagged endog. parameters for equation R</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>L1.Dp</th> <td> -0.0823</td> <td> 0.127</td> <td> -0.646</td> <td> 0.518</td> <td> -0.332</td> <td> 0.167</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L1.R</th> <td> 0.1860</td> <td> 0.099</td> <td> 1.880</td> <td> 0.060</td> <td> -0.008</td> <td> 0.380</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.Dp</th> <td> -0.0403</td> <td> 0.087</td> <td> -0.462</td> <td> 0.644</td> <td> -0.211</td> <td> 0.131</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.R</th> <td> -0.0447</td> <td> 0.100</td> <td> -0.446</td> <td> 0.655</td> <td> -0.241</td> <td> 0.151</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.Dp</th> <td> 0.0091</td> <td> 0.047</td> <td> 0.194</td> <td> 0.846</td> <td> -0.083</td> <td> 0.101</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.R</th> <td> 0.1722</td> <td> 0.099</td> <td> 1.748</td> <td> 0.080</td> <td> -0.021</td> <td> 0.365</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Loading coefficients (alpha) for equation Dp</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>ec1</th> <td> -0.5460</td> <td> 0.190</td> <td> -2.880</td> <td> 0.004</td> <td> -0.918</td> <td> -0.174</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Loading coefficients (alpha) for equation R</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>ec1</th> <td> 0.1057</td> <td> 0.167</td> <td> 0.632</td> <td> 0.527</td> <td> -0.222</td> <td> 0.434</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Cointegration relations for loading-coefficients-column 1</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>beta.0</th> <td> 1.0000</td> <td> 0</td> <td> 0</td> <td> 0.000</td> <td> 1.000</td> <td> 1.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>beta.1</th> <td> -0.1106</td> <td> 0.014</td> <td> -7.798</td> <td> 0.000</td> <td> -0.138</td> <td> -0.083</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
"Det. terms outside coint. relation & lagged endog. parameters for equation Dp \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"L1.Dp -0.5877 0.144 -4.072 0.000 -0.871 -0.305\n",
"L1.R 0.1173 0.112 1.046 0.296 -0.103 0.337\n",
"L2.Dp -0.7036 0.099 -7.124 0.000 -0.897 -0.510\n",
"L2.R 0.1875 0.113 1.653 0.098 -0.035 0.410\n",
"L3.Dp -0.8253 0.053 -15.489 0.000 -0.930 -0.721\n",
"L3.R 0.0231 0.112 0.207 0.836 -0.196 0.242\n",
" Det. terms outside coint. relation & lagged endog. parameters for equation R \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"L1.Dp -0.0823 0.127 -0.646 0.518 -0.332 0.167\n",
"L1.R 0.1860 0.099 1.880 0.060 -0.008 0.380\n",
"L2.Dp -0.0403 0.087 -0.462 0.644 -0.211 0.131\n",
"L2.R -0.0447 0.100 -0.446 0.655 -0.241 0.151\n",
"L3.Dp 0.0091 0.047 0.194 0.846 -0.083 0.101\n",
"L3.R 0.1722 0.099 1.748 0.080 -0.021 0.365\n",
" Loading coefficients (alpha) for equation Dp \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ec1 -0.5460 0.190 -2.880 0.004 -0.918 -0.174\n",
" Loading coefficients (alpha) for equation R \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ec1 0.1057 0.167 0.632 0.527 -0.222 0.434\n",
" Cointegration relations for loading-coefficients-column 1 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"beta.0 1.0000 0 0 0.000 1.000 1.000\n",
"beta.1 -0.1106 0.014 -7.798 0.000 -0.138 -0.083\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Forecasting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The VECM framework allows point forecasts as well as forecasts with confidence intervals. Both can be computed with the `predict` method. Point forecasts require a single argument for this method, specifying the forecast horizon."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.0145075 , 0.03796409],\n",
" [-0.00123764, 0.03741922],\n",
" [ 0.00309507, 0.03688557],\n",
" [ 0.02330232, 0.0354594 ],\n",
" [-0.01343658, 0.03545806]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.predict(steps=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To obtain confidence intervals too, one has to pass the desired confidence level expressed by $\\alpha$, in this example $\\alpha=0.05$."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lower bounds of confidence intervals:\n",
"[[-0.026 0.028]\n",
" [-0.013 0.021]\n",
" [-0.009 0.017]\n",
" [ 0.011 0.011]\n",
" [-0.03 0.007]]\n",
"\n",
"point forecasts:\n",
"[[-0.015 0.038]\n",
" [-0.001 0.037]\n",
" [ 0.003 0.037]\n",
" [ 0.023 0.035]\n",
" [-0.013 0.035]]\n",
"\n",
"upper bounds of confidence intervals:\n",
"[[-0.003 0.048]\n",
" [ 0.011 0.054]\n",
" [ 0.016 0.057]\n",
" [ 0.036 0.06 ]\n",
" [ 0.003 0.064]]\n"
]
}
],
"source": [
"forecast, lower, upper = vecm_res.predict(5, 0.05)\n",
"print(\"lower bounds of confidence intervals:\")\n",
"print(lower.round(3))\n",
"print(\"\\npoint forecasts:\")\n",
"print(forecast.round(3))\n",
"print(\"\\nupper bounds of confidence intervals:\")\n",
"print(upper.round(3))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The results can also be plotted via the `plot_forecast`-method."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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PBC0tLaivr+fS1sKFC1FVVRV8xzDE6XQC4JMcTyk1lPPmcDiQlJSEuLg4MU71U/yvt1Wr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\n+fNo164dwsLCNE5KlPOxMDOj9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qFXSnFyi5V48uQJrl69qnUMq8TCLBt69dY+Wv/SFBFM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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7d12cc550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vecm_res.plot_forecast(steps=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The forecasts and their confidence intervals for `Dp` are hard to identify in the last plot. We can solve this problem by specifying how many periods of the historic data shall be shown."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NjY35wIEDub29PT99+nRZmeDgYG5nZ8c5L+kZ09PTK+sp45zzFi1alOtdS05O5rq6uryo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\nAAAI0P8HQU69/f8NH4UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7d1448518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vecm_res.plot_forecast(steps=10, n_last_obs=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Testing for Granger-Causality"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test whether a variable (or a sequence of variables) is Granger-causal for another variable the `test_granger_causality`-method can be used, passing the potentially caused variable and the potentially causing variable(s) as arguments."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Granger causality f-test\n",
"=============================================================\n",
" Test statistic Critical Value p-value df\n",
"-------------------------------------------------------------\n",
" 0.875505 2.420746 0.480 (4, 184)\n",
"=============================================================\n",
"H_0: ['Dp'] do not Granger-cause R\n",
"Conclusion: fail to reject H_0 at 5.00% significance level\n"
]
},
{
"data": {
"text/plain": [
"{'conclusion': 'fail to reject',\n",
" 'crit_value': 2.4207457147957747,\n",
" 'df': (4, 184),\n",
" 'pvalue': array([[ 0.47972314]]),\n",
" 'signif': 0.05,\n",
" 'statistic': array([[ 0.87550484]])}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res. test_granger_causality(caused=\"R\", causing=\"Dp\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we may assume that inflation ($Dp$) does not Granger-cause interest ($R$), because $H_0$ cannot be rejected."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing for Instantaneous Causality"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also a test for instantaneous causality has been implemented. The corresponding code might be moved to the VAR-framework to offer this feature for VAR-processes too."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Instantaneous causality test\n",
"=======================================================\n",
" Test statistic Critical Value p-value df\n",
"-------------------------------------------------------\n",
" 1.242935 3.841459 0.265 1\n",
"=======================================================\n",
"H_0: ['Dp'] do not instantaneously cause R\n",
"Conclusion: fail to reject H_0 at 5.00% significance level\n"
]
},
{
"data": {
"text/plain": [
"{'conclusion': 'fail to reject',\n",
" 'crit_value': 3.8414588206941236,\n",
" 'df': 1,\n",
" 'pvalue': 0.26490613432197818,\n",
" 'signif': 0.05,\n",
" 'statistic': 1.2429351159760404}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.test_inst_causality(caused=\"R\", causing=\"Dp\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This result implies that an instantaneous causality does not seem to exist between the two variables."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Impulse-Response-Analysis "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Impulse-response-analysis is done with the `irf`-method which is returning an `IRAnalysis` object. Using its `irfs` attribute we get the point estimates of responses to a unit impulse."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 1. , 0. ],\n",
" [ 0. , 1. ]],\n",
"\n",
" [[-0.13371465, 0.1776207 ],\n",
" [ 0.02348687, 1.17432893]],\n",
"\n",
" [[-0.09380728, 0.25504983],\n",
" [ 0.06644178, 1.15251178]],\n",
"\n",
" [[-0.08028854, 0.06809498],\n",
" [ 0.11416248, 1.31286871]],\n",
"\n",
" [[ 0.88430983, 0.03770685],\n",
" [ 0.10228071, 1.37943072]],\n",
"\n",
" [[-0.19316105, 0.22324457],\n",
" [ 0.11812556, 1.37948052]],\n",
"\n",
" [[-0.13640952, 0.26745324],\n",
" [ 0.15793838, 1.39586844]],\n",
"\n",
" [[-0.11643367, 0.07771617],\n",
" [ 0.19382264, 1.41095801]],\n",
"\n",
" [[ 0.80848975, 0.05257701],\n",
" [ 0.17311374, 1.42028294]],\n",
"\n",
" [[-0.22176112, 0.22569022],\n",
" [ 0.18161604, 1.42322929]],\n",
"\n",
" [[-0.15351675, 0.26336671],\n",
" [ 0.21242426, 1.41822695]]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.irf().irfs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results can be plotted including also confidence intervals."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hjKQkYxs3WdcpxHlBQUEMGzaMWbNmmR2KR7Rp00aSWh+zYcMGQkNDLZfU1oRl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dgSQmQvLi4OOTk56N+/v0X/WNCKLTEW5xwDBw7E1q1ba90fOSMjA7m5ufDw8DBzdIQoD63YEkJU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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7d026b7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vecm_res.irf().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting the VAR-Representation of a VECM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each VECM \n",
"$$\\Delta y_t = \\Pi y_{t-1} + \\Gamma_1 \\Delta y_{t-1} + \\ldots + \\Gamma_{p-1} \\Delta y_{t-p+1} + u_t$$\n",
"has a corresponding VAR-representation\n",
"$$y_t = A_1 y_{t-1} + \\ldots + A_p y_{t-p} + u_t\\text{.}$$\n",
"Its parameter matrices $A_1, \\dots, A_p$ can be obtained as follows."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[-0.13371465, 0.1776207 ],\n",
" [ 0.02348687, 1.17432893]],\n",
"\n",
" [[-0.11585864, 0.07021519],\n",
" [ 0.04200101, -0.23070841]],\n",
"\n",
" [[-0.12177452, -0.16438792],\n",
" [ 0.04937598, 0.21691778]],\n",
"\n",
" [[ 0.82534077, -0.02307947],\n",
" [-0.00912162, -0.17222954]]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vecm_res.var_repr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lag Order Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Usually one has to determine a suitable lag order of a VECM before fitting it. This can be achieved with the `select_order`-function."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" VECM Order Selection \n",
"======================================================\n",
" aic bic fpe hqic\n",
"------------------------------------------------------\n",
"0 -18.44 -18.27 9.856e-09 -18.37\n",
"1 -18.64 -18.37 8.042e-09 -18.53\n",
"2 -19.61 -19.23 3.051e-09 -19.46\n",
"3 -20.66* -20.18* 1.070e-09* -20.46*\n",
"4 -20.58 -20.00 1.152e-09 -20.35\n",
"5 -20.54 -19.85 1.200e-09 -20.26\n",
"6 -20.54 -19.74 1.205e-09 -20.22\n",
"7 -20.55 -19.64 1.195e-09 -20.19\n",
"8 -20.49 -19.48 1.273e-09 -20.08\n",
"9 -20.48 -19.36 1.293e-09 -20.03\n",
"10 -20.47 -19.24 1.316e-09 -19.97\n",
"======================================================\n",
"* Minimum\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{'aic': 3, 'bic': 3, 'fpe': 3, 'hqic': 3}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"select_order(data, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All four calculated information criteria indicate that three lags were a good choice."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using the VECM Framework with Deterministic Terms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fitting the VECM to the Data (Parameter Estimation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The VECM framework is made to be capable of handling various deterministic terms. However, some of its functionality relies on existing VAR-related code. That's why some refactoring of the VAR-framework is necessary to allow for VAR processes with the same deterministic terms. When this is done, all the functiality described above will work also with deterministic terms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following we will show the VECM functions working with deterministic terms. As an example we will include seasonal terms (4 because the data is quaterly) as well as a constant inside the cointegration relation (by passing `\"ci\"`) in our model."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/vegcev/.local/lib/python3.4/site-packages/numpy/lib/shape_base.py:431: FutureWarning: in the future np.array_split will retain the shape of arrays with a zero size, instead of replacing them by `array([])`, which always has a shape of (0,).\n",
" FutureWarning)\n"
]
}
],
"source": [
"model_det = VECM(data, diff_lags=3, deterministic=\"ci\",\n",
" seasons=4, coint_rank=1)\n",
"det_vecm_res = model_det.fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, the original data can be plotted and the estimated parameters can be accessed via code just as above. This holds true also for the newly estimated parameters representing the deterministic terms. These are incorporated in the parameter summary too (seasonal terms can be found in the first two tables, deterministic terms inside the cointegration relation are at the end of the summary)."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Det. terms outside coint. relation & lagged endog. parameters for equation Dp</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>season1</th> <td> -0.0341</td> <td> 0.005</td> <td> -7.464</td> <td> 0.000</td> <td> -0.043</td> <td> -0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>season2</th> <td> 0.0015</td> <td> 0.005</td> <td> 0.318</td> <td> 0.750</td> <td> -0.008</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>season3</th> <td> -0.0179</td> <td> 0.005</td> <td> -3.814</td> <td> 0.000</td> <td> -0.027</td> <td> -0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L1.Dp</th> <td> -0.3339</td> <td> 0.141</td> <td> -2.364</td> <td> 0.018</td> <td> -0.611</td> <td> -0.057</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L1.R</th> <td> 0.0677</td> <td> 0.095</td> <td> 0.715</td> <td> 0.474</td> <td> -0.118</td> <td> 0.253</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.Dp</th> <td> -0.3874</td> <td> 0.114</td> <td> -3.399</td> <td> 0.001</td> <td> -0.611</td> <td> -0.164</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.R</th> <td> -0.0030</td> <td> 0.095</td> <td> -0.032</td> <td> 0.975</td> <td> -0.190</td> <td> 0.184</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.Dp</th> <td> -0.3457</td> <td> 0.076</td> <td> -4.524</td> <td> 0.000</td> <td> -0.495</td> <td> -0.196</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.R</th> <td> 0.0204</td> <td> 0.092</td> <td> 0.222</td> <td> 0.824</td> <td> -0.160</td> <td> 0.201</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Det. terms outside coint. relation & lagged endog. parameters for equation R</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>season1</th> <td> 0.0089</td> <td> 0.005</td> <td> 1.786</td> <td> 0.074</td> <td> -0.001</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>season2</th> <td> -0.0164</td> <td> 0.005</td> <td> -3.632</td> <td> 0.000</td> <td> -0.025</td> <td> -0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>season3</th> <td> -0.0004</td> <td> 0.005</td> <td> -0.082</td> <td> 0.934</td> <td> -0.010</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L1.Dp</th> <td> -0.2024</td> <td> 0.150</td> <td> -1.350</td> <td> 0.177</td> <td> -0.496</td> <td> 0.092</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L1.R</th> <td> 0.2724</td> <td> 0.101</td> <td> 2.709</td> <td> 0.007</td> <td> 0.075</td> <td> 0.469</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.Dp</th> <td> -0.2180</td> <td> 0.121</td> <td> -1.802</td> <td> 0.072</td> <td> -0.455</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L2.R</th> <td> -0.0164</td> <td> 0.101</td> <td> -0.162</td> <td> 0.872</td> <td> -0.215</td> <td> 0.182</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.Dp</th> <td> -0.1056</td> <td> 0.081</td> <td> -1.301</td> <td> 0.193</td> <td> -0.265</td> <td> 0.053</td>\n",
"</tr>\n",
"<tr>\n",
" <th>L3.R</th> <td> 0.2253</td> <td> 0.098</td> <td> 2.303</td> <td> 0.021</td> <td> 0.034</td> <td> 0.417</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Loading coefficients (alpha) for equation Dp</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>ec1</th> <td> -0.6317</td> <td> 0.167</td> <td> -3.791</td> <td> 0.000</td> <td> -0.958</td> <td> -0.305</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Loading coefficients (alpha) for equation R</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>ec1</th> <td> 0.3972</td> <td> 0.177</td> <td> 2.245</td> <td> 0.025</td> <td> 0.050</td> <td> 0.744</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Cointegration relations for loading-coefficients-column 1</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>beta.0</th> <td> 1.0000</td> <td> 0</td> <td> 0</td> <td> 0.000</td> <td> 1.000</td> <td> 1.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>beta.1</th> <td> -0.2508</td> <td> 0.044</td> <td> -5.671</td> <td> 0.000</td> <td> -0.338</td> <td> -0.164</td>\n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 0.0107</td> <td> 0.003</td> <td> 3.142</td> <td> 0.002</td> <td> 0.004</td> <td> 0.017</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
"Det. terms outside coint. relation & lagged endog. parameters for equation Dp \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"season1 -0.0341 0.005 -7.464 0.000 -0.043 -0.025\n",
"season2 0.0015 0.005 0.318 0.750 -0.008 0.011\n",
"season3 -0.0179 0.005 -3.814 0.000 -0.027 -0.009\n",
"L1.Dp -0.3339 0.141 -2.364 0.018 -0.611 -0.057\n",
"L1.R 0.0677 0.095 0.715 0.474 -0.118 0.253\n",
"L2.Dp -0.3874 0.114 -3.399 0.001 -0.611 -0.164\n",
"L2.R -0.0030 0.095 -0.032 0.975 -0.190 0.184\n",
"L3.Dp -0.3457 0.076 -4.524 0.000 -0.495 -0.196\n",
"L3.R 0.0204 0.092 0.222 0.824 -0.160 0.201\n",
" Det. terms outside coint. relation & lagged endog. parameters for equation R \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"season1 0.0089 0.005 1.786 0.074 -0.001 0.019\n",
"season2 -0.0164 0.005 -3.632 0.000 -0.025 -0.008\n",
"season3 -0.0004 0.005 -0.082 0.934 -0.010 0.009\n",
"L1.Dp -0.2024 0.150 -1.350 0.177 -0.496 0.092\n",
"L1.R 0.2724 0.101 2.709 0.007 0.075 0.469\n",
"L2.Dp -0.2180 0.121 -1.802 0.072 -0.455 0.019\n",
"L2.R -0.0164 0.101 -0.162 0.872 -0.215 0.182\n",
"L3.Dp -0.1056 0.081 -1.301 0.193 -0.265 0.053\n",
"L3.R 0.2253 0.098 2.303 0.021 0.034 0.417\n",
" Loading coefficients (alpha) for equation Dp \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ec1 -0.6317 0.167 -3.791 0.000 -0.958 -0.305\n",
" Loading coefficients (alpha) for equation R \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ec1 0.3972 0.177 2.245 0.025 0.050 0.744\n",
" Cointegration relations for loading-coefficients-column 1 \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"beta.0 1.0000 0 0 0.000 1.000 1.000\n",
"beta.1 -0.2508 0.044 -5.671 0.000 -0.338 -0.164\n",
"const 0.0107 0.003 3.142 0.002 0.004 0.017\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"det_vecm_res.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Forecasting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Forecasting works as in the case of no deterministic terms. Here we will only show the plotted results."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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nn3wCzjn9U9Eily9fxvPnz6vdEPb1UCUlY9qlqKgIGzduxN69eyst83rxV0rG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\nPKQyJiIiIuIhlTERERERD/1/BMnhMHEdOGEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7d1456da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"det_vecm_res.plot_forecast(steps=10, n_last_obs=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Testing for Granger-Causality"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing for Granger-causality relies on the VAR-framework and thus does not allow for deterministic terms until `VAR` can handle them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing for Instantaneous Causality"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing for instantaneous causality relies on the VAR-framework and thus does not allow for deterministic terms until `VAR` can handle them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Impulse-Response-Analysis "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculating point estimates for impulse responses works also when deterministic terms are assumed. We will only plot the results here."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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+8EG+Ov+yy+Css2CPPYquSFKjsQ1BUr0w2Naxhx+GAw/M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\nRNvhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7d159f4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"det_vecm_res.irf().plot(plot_stderr=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculating confidence intervals and plotting them will be available as soon as the `VAR` framework is refactored to be capable of handling arbitrary deterministic terms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting the VAR-Representation of a VECM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also with deterministic terms the VECM framework is able to produce the corresponding VAR representation."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0.03433973, 0.22617928],\n",
" [ 0.19486172, 1.17273842]],\n",
"\n",
" [[-0.0535351 , -0.07072214],\n",
" [-0.01565624, -0.28875262]],\n",
"\n",
" [[ 0.04174712, 0.02343397],\n",
" [ 0.11248905, 0.24162739]],\n",
"\n",
" [[ 0.34569836, -0.02042696],\n",
" [ 0.1055512 , -0.22525579]]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"det_vecm_res.var_repr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lag Order Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lag order selection relies on the VAR-framework and thus does not allow for deterministic terms until VAR can handle them."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@saravanansaminathan
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dta.load_pandas();

While using this line I encountered this error "module 'data' has no attribute 'load_pandas' " Which dataset you used for this problem

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