VECM example

VECM_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vector Error Correction Models (VECM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we will introduce some of the `vecm` module's functionality which helps analyzing a vector error correction model (VECM)\n",
    "$$\\Delta y_t = \\alpha \\beta^T y_{t-1} + \\Gamma_1 \\Delta y_{t-1} + \\dots + \\Gamma_{p-1} \\Delta y_{t-p+1} + u_t$$\n",
    "where $\\alpha, \\beta \\in \\mathbb{R}^{K \\times r}$ and $\\Gamma_i \\in \\mathbb{R}^{K \\times K}$ for $i = 1, \\dots, p-1$ are the parameters and $u_t$ is $K$-dimensional white noise. Both $\\alpha$ and $\\beta$ have rank $r$ - the so called cointegration rank.\n",
    "\n",
    "Before we demonstrate the module's functionality we take care of the necessary imports."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.tsa.vecm.vecm import *\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.datasets.interest_inflation.data as d\n",
    "df = d.load_pandas().data\n",
    "dates = df[[\"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",
    "data = df[[\"Dp\", \"R\"]]\n",
    "data.index = pandas.DatetimeIndex(quarterly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model specification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deterministic terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fba4cfae978>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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l4IpXDKyjZa3RwnMXFlzHmZagFmMArMVGDJRSHJvIYp9ujQKQWmgLxTrWdZDOd2gsg8dP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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4d071630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax = plt.subplot(211)\n",
    "ax.plot(data.index, data[\"Dp\"])\n",
    "ax = plt.subplot(212)\n",
    "ax.plot(data.index, data[\"R\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the two variables \"Dp\" (inflation) and \"R\" (interest) so in our example $K=2$ holds.\n",
    "\n",
    "In the plot we see that the variable \"Dp\" has a strong seasonal effect which we will consider by specifying `seasons=4` in several of the following input cells. We also notice that all observations of \"R\" are greater than zero, so we will include an intercept. It can be shown that a VECM with an intercept can be written as\n",
    "$$\\Delta y_t = \\alpha \\bar{\\beta}^T \\begin{pmatrix}y_{t-1}\\\\1\\end{pmatrix} + \\Gamma_1 \\Delta y_{t-1} + \\dots + \\Gamma_{p-1} \\Delta y_{t-p+1} + u_t$$\n",
    "with the matrix $\\bar{\\beta}^T = \\begin{pmatrix}\\beta^T & -\\beta^T\\mu\\end{pmatrix}$ and $\\mu \\in \\mathbb{R}^K$ being the intercept. So the intercept only appears inside the cointegration relation $\\alpha \\beta^T y_{t-1}$. In the following cells we will specify this term using `deterministic=\"ci\"` where the _i_ in `\"ci\"` stands for _inside_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lag order selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `select_order()` we can choose the lag order according to various information criteria (AIC, BIC, HQIC, and FPE). To find the best number of lagged differences according to a specific information criterion we can use the `summary()` method of the returned `LagOrderResults` object. We then look for the star (\\*) (=minimal value) in the column of interest in the `SimpleTable`. The left cell of the star's row shows the number of lagged differences to choose. So we would choose a lag order of 3 in the following cell if we were interested in the AIC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vegcev/D/TUG/Masterarbeit/GSoC/statsmodels-yogabonito/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>VECM Order Selection (* highlights the minimums)</caption>\n",
       "<tr>\n",
       "   <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th>  <td>    -20.67</td>  <td>    -20.30*</td> <td> 1.055e-09</td>  <td>    -20.52*</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th>  <td>    -20.68</td>  <td>    -20.19</td>  <td> 1.050e-09</td>  <td>    -20.48</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>  <td>    -20.56</td>  <td>    -19.97</td>  <td> 1.182e-09</td>  <td>    -20.32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>  <td>    -20.76*</td> <td>    -20.06</td>  <td> 9.717e-10*</td> <td>    -20.47</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>  <td>    -20.64</td>  <td>    -19.84</td>  <td> 1.089e-09</td>  <td>    -20.32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>  <td>    -20.60</td>  <td>    -19.69</td>  <td> 1.143e-09</td>  <td>    -20.23</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th>  <td>    -20.63</td>  <td>    -19.62</td>  <td> 1.109e-09</td>  <td>    -20.22</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>7</th>  <td>    -20.44</td>  <td>    -19.32</td>  <td> 1.350e-09</td>  <td>    -19.98</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>8</th>  <td>    -20.33</td>  <td>    -19.10</td>  <td> 1.514e-09</td>  <td>    -19.83</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>9</th>  <td>    -20.39</td>  <td>    -19.06</td>  <td> 1.428e-09</td>  <td>    -19.85</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>10</th> <td>    -20.14</td>  <td>    -18.70</td>  <td> 1.844e-09</td>  <td>    -19.56</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lag_order = select_order(data=data, maxlags=10, deterministic=\"ci\", seasons=4)\n",
    "lag_order.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The string representation shows only the rank to choose according to the different information criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.tsa.vector_ar.var_model.LagOrderResults object. Selected orders are: AIC -> 3, BIC -> 0, FPE -> 3, HQIC -> 0>\n"
     ]
    }
   ],
   "source": [
    "print(lag_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course it is possible to access the lag order direcly via the `LagOrderResults` object as the following cell shows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 0, 3, 0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lag_order.aic, lag_order.bic, lag_order.fpe, lag_order.hqic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cointegration rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `select_coint_rank` helps us choosing the cointegration rank. The `rank` attribute of the resulting `CointRankResults` object gives us the desired information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_test = select_coint_rank(data, 0, 3, method=\"trace\",\n",
    "                              signif=0.95)\n",
    "rank_test.rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see more details we can use the `summary()` method of `CointRankResults`. Each row of the resulting `SimpleTable` shows one test with the null hypothesis \"The cointegration rank is r_0\" and $H_1$ \"The cointegration rank is greater than r_0 and $\\leq$ r_1\". The last row contains the information about the cointegration rank to choose. If its test statistic is less than its critical value, use r_0 as the cointegration rank. Otherwise use r_1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Johansen cointegration test using trace test statistic with 95% significance level</caption>\n",
       "<tr>\n",
       "  <th>r_0</th> <th>r_1</th> <th>test statistic</th> <th>critical value</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>0</td>   <td>2</td>          <td>17.17</td>          <td>15.49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1</td>   <td>2</td>          <td>3.032</td>          <td>3.841</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_test.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing a `CointRankResults` object gives a string representation of the `SimpleTable`. (__Question:__ Which of the string representations is more preferable: that of `LagOrderResults` or that of `CointRankResults`?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Johansen cointegration test using trace test statistic with 95% significance level\n",
      "=====================================\n",
      "r_0 r_1 test statistic critical value\n",
      "-------------------------------------\n",
      "  0   2          17.17          15.49\n",
      "  1   2          3.032          3.841\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(rank_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fit a VECM to the data we first build a `VECM` object where we define\n",
    "  1. the deterministic terms\n",
    "  2. the lag order, and\n",
    "  3. the cointegration rank.\n",
    "We have discussed all three topics above. We have got different suggestions for the lag order depending on the information criterion we used. We will follow the Akaike information criterion (AIC) by using `lag_order.aic`.\n",
    "\n",
    "Once we have the `VECM` instance, we can call its `fit()` method which returns a `VECMResults` object. This object offers a `summary()` method listing all of the model's parameters with the corresponding standard errors etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vegcev/D/TUG/Masterarbeit/GSoC/statsmodels-yogabonito/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "model = VECM(data, deterministic=\"ci\", seasons=4,\n",
    "             diff_lags=lag_order.aic,  # =3\n",
    "             coint_rank=rank_test.rank)  # =1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vecm_res = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Det. terms outside the 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>    0.0162</td> <td>    0.005</td> <td>    3.554</td> <td> 0.000</td> <td>    0.007</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>season2</th> <td>    0.0177</td> <td>    0.005</td> <td>    3.690</td> <td> 0.000</td> <td>    0.008</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>season3</th> <td>    0.0341</td> <td>    0.005</td> <td>    7.464</td> <td> 0.000</td> <td>    0.025</td> <td>    0.043</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 the 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>    0.0074</td> <td>    0.005</td> <td>    1.518</td> <td> 0.129</td> <td>   -0.002</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>season2</th> <td>   -0.0019</td> <td>    0.005</td> <td>   -0.381</td> <td> 0.703</td> <td>   -0.012</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>season3</th> <td>   -0.0015</td> <td>    0.005</td> <td>   -0.318</td> <td> 0.750</td> <td>   -0.011</td> <td>    0.008</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.1</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.2</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 the coint. relation & lagged endog. parameters for equation Dp\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "season1        0.0162      0.005      3.554      0.000       0.007       0.025\n",
       "season2        0.0177      0.005      3.690      0.000       0.008       0.027\n",
       "season3        0.0341      0.005      7.464      0.000       0.025       0.043\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 the coint. relation & lagged endog. parameters for equation R\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "season1        0.0074      0.005      1.518      0.129      -0.002       0.017\n",
       "season2       -0.0019      0.005     -0.381      0.703      -0.012       0.008\n",
       "season3       -0.0015      0.005     -0.318      0.750      -0.011       0.008\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.1         1.0000          0          0      0.000       1.000       1.000\n",
       "beta.2        -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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, all parameters can be accessed via the `VECMResults` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.63174988],\n",
       "       [ 0.39724572]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.16663211],\n",
       "       [ 0.17693968]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.stderr_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtaining a forecast for the next $i$ periods is as easy as calling the `predict()` method passing $i$ as the `steps` argument. To get confidence intervals, we also pass the desired confidence level to the argument `alpha`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02236238,  0.03961484],\n",
       "       [-0.00390943,  0.04075971],\n",
       "       [ 0.00331985,  0.04018359],\n",
       "       [ 0.02437677,  0.03881549],\n",
       "       [-0.02489008,  0.04001414]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.predict(steps=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forecast:\n",
      "[[-0.02236238  0.03961484]\n",
      " [-0.00390943  0.04075971]\n",
      " [ 0.00331985  0.04018359]\n",
      " [ 0.02437677  0.03881549]\n",
      " [-0.02489008  0.04001414]]\n",
      "lower:\n",
      "[[-0.03177701  0.02961783]\n",
      " [-0.01359287  0.02532279]\n",
      " [-0.00657373  0.02100911]\n",
      " [ 0.01428314  0.01575236]\n",
      " [-0.03587008  0.01341332]]\n",
      "upper:\n",
      "[[-0.01294775  0.04961184]\n",
      " [ 0.005774    0.05619664]\n",
      " [ 0.01321343  0.05935806]\n",
      " [ 0.03447039  0.06187863]\n",
      " [-0.01391009  0.06661495]]\n"
     ]
    }
   ],
   "source": [
    "vecm_res.predict(steps=5, alpha=0.05)\n",
    "for text, vaĺues in zip((\"forecast\", \"lower\", \"upper\"), vecm_res.predict(steps=5, alpha=0.05)):\n",
    "    print(text+\":\", vaĺues, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also print the forecasts using the `plot_forecast()` method. In order to have only the point forecasts plotted, we use `plot_conf_int=False`. To restrict the plotted history to the last $i$ observations, we pass this value to the `n_last_obs` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AnUiZVaW0qhI8OHXqFN761rcWjAnQS8pkT1+WkpQBU9+R/b8IdJ6YZEqZdVwy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3AAAg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\nAUnKhBBCCCHsgCRlQgghhBB24P8BTFhrGk/12wMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4ceb8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vecm_res.plot_forecast(steps=5, plot_conf_int=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U4KJMEAQzAIEAOgBwBNBLEATHdy7rAKDu688QAKteH88F8BMROQJoBuBHFW1ZIXPkyBEE\nBQVh+vTpSq+u3uTp6QmA1ysrbObNm4cSJUpg9Oj3/wzm5eWFhIQEhIaGGjAZ07e4uDisX78effv2\nReXKlZXOe3h4ACg6T0VMSVJSElatWoVu3bqhTp06GrV9+fIlZDIZ/vvvv/dec+rUKbRr107lmmRS\nqbTI/J3QxZMydwDhRBRBRNkAdgLo9M41nQBsoVcuACglCIIdET0joqsAQEQpAO4AUP6XygoNhUIB\nX19f1KxZEz/88MMHr23UqBHKlCnDrzALkfv372Pnzp0YPnw4ypUr997reFxZ4bRixQpkZGRgwoQJ\nKs+7ublBEAR+hWmEVq9ejeTkZEyaNEnjtqtWrcLp06fzZ1mr8qHB/m5uboiOjkZ0dLTGfZsaXRRl\nlQE8fuPXT6BcWH30GkEQagBoCkDlv0ZBEIYIghAsCELwixcvChiZieWPP/5ASEgI5syZ89HtMyQS\nCVq1asVPygqRefPmoVixYvjpp58+eF2VKlVQt25dXkS2EElLS8OKFSvw1VdfoUGDBiqv+eSTTxAQ\nEIAOHToYOB37kMzMTAQEBKBt27b5Y7zUlZGRgYCAALRv3x5NmzZ973X29vaws7N7b1EGqC7YChuj\nGOgvCIINgD0AxhBRsqpriGgtEUmJSFq+fHnDBmQ6kZ2djWnTpsHZ2Rm9evVSq42npyciIiLw+PHj\nj1/MjNrDhw+xdetWDBkyBBUrVvzo9V5eXjh9+jRyc3MNkI7p24YNGxAfH//RJy2jRo2CVCo1UCqm\nji1btiAmJkblVkgfs3HjRsTGxmLy5MkfvVYqlaqcZdmkSROYmZkViVeYuijKngKo+savq7w+ptY1\ngiBY4FVB9jsR/aWDPMxIrVu3DhEREfDz84NEot5fPd4Hs/CYP38+zMzM3vvq6l1eXl5ITk7GtWvX\n9JyM6Vtubi6WLFmCFi1aoHnz5h+8Nj09HceOHSsy61KZgipVqmDAgAH5wwrUlZubi0WLFuHTTz9V\nudzFu6RSKe7evYuUlJS3jltbW6Nhw4b8pExNlwHUFQShpiAIlgB6Atj3zjX7APR/PQuzGYAkInom\nCIIAYD2AO0S0VAdZmJFKTU3Fzz//DJlMhvbt26vdzsnJCaVKleKizMQ9efIEGzduxKBBg1QO8FYl\n7xsAv8I0fbt27UJUVJRa45HCwsLQtm1bHD161ADJmDp8fHywceNGvPqWrT65XI7Ro0fj559/Vqtt\n3759ERQUhGLFiimda9eu3QfHoRYaRFTgDwAfAPcAPAAw9fWxoQCGvv5vAa9maD4AcAOA9PXxlgAI\nQCiAkNcfn4/15+rqSsy0/PzzzwSAzp8/r3Hbjh07Ut26dfWQihnKyJEjydzcnCIjIzVq5+joSJ9/\n/rmeUjFDUCgU5OzsTI6OjiSXyz96fU5ODllZWdHo0aMNkI59iEKhoDVr1lBiYqLYUUwegGBSo54y\n11FhdxDAwXeOrX7jvwnAjyranX1dsLFC7MWLF1i0aBG6dOmCZs2aadxeJpNh//79iI6OVlp0kBm/\nmJgYrFu3Dv3790f16tU1auvl5YWNGzciOzv7oxNDmHE6cuQIrl+/jo0bN6o1bMHc3Byurq64dOmS\nAdKxDzl79ix++OEHSCQSDB48WKO2QUFBePjwIfr37w9zc/VLjWPHjiExMRHffPONyvNEpPETO1Ni\nFAP9WeE2b948pKWl4ZdfftGqPa9XZtoWL16M7OxstQb6vsvb2xvp6elFYoBvYbVw4ULY29ujd+/e\narfx8PDA1atXkZ2drcdk7GNatmyJAwcO5G8ary4iwtSpUzFnzhyN+1yxYgWmT5+udFyhUMDZ2Vmr\nryOmhIsypldRUVFYuXIlBg4c+N5p8B/TpEkT2Nra8nplJujFixdYtWoVevfurfGCk8Crp6SCIPB6\nZSYqODgYJ06cwNixYzV60unu7o6srCxePFhkgiDAx8dH5RivDzl9+jTOnz+PCRMmaPSUDHg12D8s\nLAzJyW8vxCCRSNC0aVPY2dlpdD9Tw0UZ06sZM2ZAIpFg1qxZWt/DzMwMLVu25CdlJsjf3x8ZGRmY\nMmWKVu3Lli0LZ2dnHuxvohYuXIhPPvlE482r27RpgxMnTsDRkTd4Ecv3338PPz8/rdr6+fmhQoUK\nGDhwoMZt8xaRVTXretOmTR/cCaQw4KKM6c2NGzewdetWjBw5ElWqVCnQvTw9PREWFoaYmBgdpWP6\nlpiYiBUrVqBbt25aPyUFXo0rO3fuHDIzM3WYjulbeHg49uzZg2HDhsHW1lajtmXKlIGXl9cHV4Bn\n+nP//n2sX78eqampGre9evUqDh8+jDFjxsDKykrj9h9a2R94NaOzML/W5qKM6c2UKVPwySefaLXg\n4Lt4vTLTs3z5cqSkpGDatGkFuo+XlxeysrJw4cIFHSVjhrBkyRKYm5tr/WTj8uXLWLqUV0oSw6JF\ni2BpaYlRo0Zp3DYhIQEuLi4YNmyYVn1XqFAB1apVU7kHZkREBD755BPs2rVLq3ubAi7KmF6cPXsW\n//77LyZNmoQyZcoU+H4uLi6wsbHhosxEJCcnIyAgAJ06dYKTk1OB7tW6dWtIJBJ+hWlCnj9/jo0b\nN+Lbb79FpUqVtLrH0aNH8dNPPyExMVHH6diHPHv2DJs3b8agQYPU2nnjXW3atMGVK1dQqlQprTOc\nOHECGzZsUDperVo1KBSKQr2ILBdlTOeICJMmTYK9vb1WP2mpYm5uzuPKTEhgYCBevnypchaVpj75\n5BO4urryYH8T8uuvvyI7Oxvjx4/X+h4eHh4AwEtjGFhAQAByc3O1+rM7ceIE0tPTC5yhdu3aKicX\nmJubw8XFpVDPxuaijOnc/v37ce7cOcycOVOnY0JkMhlu376N2NhYnd2T6V5aWhqWLl2KDh065I8P\nKSgvLy9cvHgRaWlpOrkf05+UlBQEBgaiS5cuqFevntb3cXNzgyAIuHjxog7TsQ95+fIlVq1ahe7d\nu6NWrVoatX369Cnat29f4OEKwKu1DceNG6fyFaZUKsW1a9cK7Z64XJQxnZLL5ZgyZQrq1auHQYMG\n6fTeeePKTp8+rdP7Mt1avXo14uLidPLFOY+3tzdycnLw33//6eyeTD9+++03vHz5EhMnTizQfWxt\nbdGgQQN+UmZAq1evRkpKilrbYb3L398fcrkcI0aMKHAOc3Nz+Pv74/jx40rn3NzckJGRgdu3bxe4\nH2PERRnTqa1bt+LWrVv45ZdfNF6f5mOkUimsra35FaYRy8jIwOLFi+Ht7f3Rjac10aJFC5ibm/Mr\nTCOXnZ2NpUuXQiaT5b9+LAgPDw9cu3Ytbzs/pkeZmZkICAjA559/jiZNmmjUNiEhAWvWrEHPnj01\nfsKmSrly5VC9enWVY8ekUikAFNpXmFyUMZ3JzMzEjBkz4Obmhq5du+r8/hYWFmjRogUvImvE1q9f\nj5iYGJ2MJXuTjY0N3N3duSgzcjt37sSTJ0+0etKiyoIFC/DgwYNCva2OsTh8+DCeP3+u1Wz5wMBA\npKam6mSmfR6pVIorV64oHa9bty5sbW0L7WB/LsqYzqxcuRKPHz/G/Pnz9fZFVCaT4ebNm4iLi9PL\n/Zn2srKysGDBArRs2TL/VbMueXt7Izg4WGmlb2YcFAoFFi5ciMaNG6N9+/Y6uWf58uVRvHhxndyL\nfVinTp0QGhqq1b/dEydO4Msvv0Tjxo01bjtkyBCsX79e6birqysePHigNPtWIpHA1dWVn5Qx9iFJ\nSUn45Zdf0K5dO3h7e+utn7x9MM+cOaO3Pph2Nm/ejCdPnmD69Ol6Kcq9vLwgl8v5z95IHTp0CLdu\n3cLEiRN1+uc/c+ZMBAYG6ux+TJlcLgcANG7cWKs/u+PHj2PLli0atztz5gzWrVuHjRs3Kp2TSqUo\nUaIEHjx4oHTOzc0NoaGhyMrK0rhPY8dFGdOJRYsWISEhAfPnz9drP25ubrCysuJXmEYmJycHfn5+\ncHNzQ9u2bfXSx6effgpLS0t+hWmkFi5ciGrVqqFHjx46vW9QUBC2bdum03uy/0dEaNGiBWbPnq1x\n25ycHCQlJUEikaB06dIat583bx4AICQkJL8wzOPl5YWkpKT8MWRv6tq1K5YsWVIoZ2ByUcYK7Nmz\nZ/D390fPnj3RtGlTvfZlaWmJ5s2b82B/I7N9+3ZERkbq7SkZAFhZWaF58+a8iKwRunDhAk6fPo1x\n48bBwsJCp/d2d3fHtWvXCvXWOmJKSUmBnZ0dqlevrnHb33//HdWrV0d4eLhWfY8cORIdO3ZEWloa\n7t2799Y5c3NzmJmZqWzn7u6OkSNHokSJElr1a8y4KGMFNmfOHGRnZ2POnDkG6U8mkyE0NBQJCQkG\n6Y99mFwux7x589CkSRN8+eWXeu3Ly8sLISEh/GdvZBYuXIjSpUvju+++0/m9PTw8kJWVhevXr+v8\n3kVdXFwcbG1tsXfvXgwYMECjtgqFAgsWLECNGjVQu3Ztrfr38fHJf1qmak2y3377DZ07d1bZ9tGj\nR4VysD8XZaxAwsPDsW7dOgwZMgR16tQxSJ8ymQxExGOLjMSuXbtw7949TJs2Te+z5Ly8vEBEvFad\nEQkLC8Pff/+NESNGwMbGRuf3z1tagxeR1Z20tDQMHDgQLi4uWm9j9ffff+Pu3bvw9fXV+N99WFgY\nJk+ejISEBNSvXx9WVlYqZ1omJCTgn3/+UflD2A8//KCXHwLExkUZK5Bp06bB0tJS50sgfIi7uzuK\nFy/OrzCNgEKhwC+//IKGDRuiS5cueu/Pw8MDVlZW/ArTiCxevBjFihXTyaKhqlStWhW1a9fmPTB1\n5Pbt23B3d8fmzZsxcOBA2NraanwPIsL8+fNRu3ZtdOvWTeP2f/75Z/52Tubm5nB2dlZZlOXtCKLq\n3Pjx4/OfshUmul3dkxUpV65cwR9//IFp06ZpvemwNooXL45mzZrxYH8jsHfvXty6dQvbt2+HRKL/\nn/EsLS3RsmVLHuxvJJ49e4YtW7Zg8ODBqFChgl76EAQB9+/f57XKdGDTpk348ccfYWNjgyNHjqBN\nmzZa3efChQu4fPkyVq9erdUi4VOnTkXv3r3z/8706tULL168ULrOxcUFABAcHKw0geizzz7TIrkJ\nICKT+7i6uhITX5s2bahs2bKUlJRk8L5nzpxJgiBQYmKiwftmrygUCmrSpAnVrVuXcnNzDdbvvHnz\nCAA9f/7cYH0y1SZNmkQSiYQePHggdhT2AampqfTtt98SAPL09KTo6OgC3U+hUNDx48cpIyND47bp\n6ekaXV+7dm3q2rWrygwnT56kK1euaJxBDACCSY36hl9fMq0cO3YMx44dw9SpU7V6/F1Qnp6eICKc\nPXvW4H2zVw4cOICQkBBMmTLlvbOk9CFvHTx+UiqupKQkrFq1Ct98841Ottb5kPv378Pd3R3Hjh3T\naz+F0a1bt+Du7o4tW7Zg5syZOHbsGOzs7Ap0T0EQ4O3trfHCvrGxsahSpQq2b9+udC4rKwsvX75U\nOt6uXTuV32MEQUCfPn2wdOlSjTIYOy7KmMYUCgV8fX1RrVo1DBs2TJQMHh4esLS05G/MIiEizJkz\nBzVq1ECfPn0M2rerqytKlizJrzBFtnbtWiQnJxd443F1VKxYEcHBwTh37pze+ypMoqOj4e7ujvj4\neBw9ehSzZs0q8A9Q/fv3x8yZM7Vqu2zZMiQmJuaPFcsjl8tRoUIFzJ07V6nNypUrsWHDBpX3k0ql\nhW5lfy7KmMZ2796NK1eu4OeffxZtCxQrKyt4eHjwYH+RHD16FJcuXcLkyZN1vi7Vx5ibm6N169Y8\n2F9EWVlZ8Pf3R5s2bfLH/eiTra0tHB0deQammhQKBQDA3t4eAQEBCAkJ0ckYrLt372Lbtm1KC72q\nIykpCStWrEC3bt3g4ODw1jkzMzOMHTsWzZs3f297UrEpvZubG+7du4ekpCSN8xgtdd5xfuwDoD2A\nMADhAHxVnBcALH99PhSAyxvnNgCIBXBT3f54TJl4srOzqU6dOtSoUSODjiNSZfr06SSRSEQZ01aU\nKRQKatmyJVWpUoUyMzNFybB48WICQE+fPhWl/6Ju/fr1BICOHDlisD4HDRpEZcuWJYVCYbA+TdHd\nu3fJ2dljxfJdAAAgAElEQVSZzp07p/N7DxgwgKysrCg2NlbjtnljQa9evapRu6ysLGrYsCH5+fkp\nnfvf//5HAOj48eMa5zE0GGpMmSAIZgACAXQA4AiglyAIju9c1gFA3defIQBWvXFu0+uijpmA9evX\nIzw8HPPmzTPoOCJVZDIZFAoFjyszsFOnTuHs2bOYOHEiihUrJkoGLy8vAOBXmCLI23i8adOmWs/e\n04aHhwfi4+MRERFhsD5NUfHixZGdna3zHRAeP36Mbdu2YfDgwShfvrxGbdPT0+Hv748OHTq8d9cX\nIkJUVBSSk5PfOm5paYns7GxcunRJqU3eFkyFaRFZXby+dAcQTkQRRJQNYCeATu9c0wnAltcF4wUA\npQRBsAMAIjoNgJfnNgFpaWmYPXs2WrRoofeV29Xx6aefwsLCgl9hGtjcuXNRsWJFDB48WLQMzs7O\nKF26NL/CFMH+/fsRFham843HP6Z58+Zo27Yt0tLSDNanqUhLS8OyZctARKhevTpu3rwJmUym0z6W\nLFkC4NX6YJpav349Xrx4gSlTprz3mpCQENSoUQOHDh1SOufq6qqy8Cpbtixq1qxZqMaV6aIoqwzg\n8Ru/fvL6mKbXfJAgCEMEQQgWBCFY1XomTP+WLVuGmJgYLFiwwCjWDLK2toa7uzsXZQZ0/vx5HD9+\nHBMmTICVlZVoOczMzCCTyfhJmYERERYsWICaNWtqtWhoQTRq1AhHjhyBk5OTQfs1djdv3oSbmxvG\njh2L8+fPA4Be1gzs3bs3/P39Ua1aNY3aZWdnY9GiRWjVqhVatmz53usaNmwIS0tLlQvFSqVSPH78\nGLGxsUrn3Nzc+EmZGIhoLRFJiUiq6aNTVnDx8fFYsGABOnbsiBYtWogdJ59MJkNwcDBSUlLEjlIk\nzJkzB+XKlcPQoUPFjgIvLy88fPgQkZGRYkcpMv777z+cP38eP/30k1aLhupCZmamKP0aGyLCxo0b\n4e7ujoSEBBw9evSDA+ULyt3dXatdG7Zv347Hjx9/8CkZ8Oo1ZePGjVXugfmhlf2lUikiIyNVLj5r\ninRRlD0FUPWNX1d5fUzTa5gR8/PzQ0pKitFta+Hp6Qm5XM5T5Q0gODgYhw4dwrhx41CiRAmx4+Sv\nV8ZPywxnwYIFKFeuHAYOHChK/4sXL0aZMmWQlZUlSv/GIjU1Fd9++y0GDRqETz/9VGezK1VJSUnB\nyJEjERUVpVX7GzduwMXFBZ9//vlHr3V1dcXVq1eVZlq6uLigS5cuKFmypFIbNzc3AKoLNpOkzmyA\nD33waqumCAA1AVgCuA6g4TvXfAHgEF7NwmwG4NI752uAZ18araioKCpWrBh9++23YkdRkpqaSubm\n5uTr6yt2lEKvU6dOVKpUKaOZ7apQKKh8+fLUr18/saMUCTdv3iQANHv2bNEy/PnnnwSALl68KFoG\nsd24cYPq169PgiDQrFmz9D4LfsmSJQSALly4oPU91F3Ff/Xq1QRAox0iMjIy6N69eySXy7WNZxAw\n1OxLIsoFMALAYQB3AOwioluCIAwVBCHvHcfB14VbOIB1AIbntRcEYQeA8wAcBEF4IghC4dv23cTN\nmjULRITZs2eLHUVJiRIlIJVKeVyZnoWGhuKff/7B6NGjRdnBQRVBEODp6YmgoCCVaxgx3Vq0aBGs\nra3x448/ipbBw8MDAIrkemVEhPXr18Pd3R2JiYk4duwYZs6cqddZ8FlZWViyZAm8vLzy/9+ri4jw\n4MEDAFB7/Gnea0pVrzCBV8No3lW8eHHUrVvXIHvvGoQ6lZuxffhJmeHcunWLJBIJjR07Vuwo7+Xr\n60vm5uaUmpoqdpRCq3v37lSyZElKSEgQO8pbVq1aRQDo3r17Ykcp1B4/fkzm5uY0atQoUXMoFAqy\ns7OjPn36iJpDDDk5OdSsWTPy9vamZ8+eGaTPdevWab0e3aFDh0gQBI3WEMvMzHzvm481a9YQAJW/\n9+PHj9Po0aM1zmhI4L0vmS5MmTIFNjY2Hx2kKSaZTIbc3FweV6Ynd+7cwZ9//okRI0agdOnSYsd5\nC69XZhj+/v4gIowbN07UHIIgwN3dvUg9Kbt58ybi4+Nhbm6Of//9F0eOHEGlSpX03q9cLsfChQvh\n4uKi1Xp0tWrVwogRIz444/JdxYoVQ6NGjVSOD2vQoAEA1WPHbt68iRUrVqicnWlquChj73Xu3Dn8\n888/mDBhAsqVKyd2nPdq0aIFzMzM+BWmnsybNw9WVlYYO3as2FGU1KtXD3Z2dlyU6VFiYiLWrl2L\nnj17onr16mLHQb9+/TBw4MAi8cr66dOn8PDwyF8brGzZsgZbtDslJQVSqRRTp07VagmkevXqYfny\n5bC0tNSo3fsG+zdt2hSCIKgsygYOHIjk5GRUqFBB45zGRpw5zczoERF8fX1RsWJFo/xm/KaSJUvC\n1dWVizI9CA8Px/bt2zFmzBiNV/E2BEEQ4O3tjWPHjoGIjGL9vMJm1apVSE1NNcjG4+ro2rWr2BH0\nTqFQQCKRoHLlyli2bJkoi3WXKlUK27dv16rt/Pnz0aFDBzg7O2vcdvjw4ejRo4fSv2cbGxvUr19f\n5ZpkqmZlmip+UsZUOnjwIM6cOYMZM2YYxfIHHyOTyXDx4kWkp6eLHaVQ8fPzg4WFhVareBuKl5cX\nnj9/jjt37ogdpdDJzMzEsmXL0L59e6NatDU+Pj5/EHlhc+PGDTRp0iR/IdjBgwcb5HXlm0JCQnDj\nxg2t206ePBkHDhzQqr2Liwvatm2rcuC+q6vre5e+WLFiBSZPnqxVn8aEizKmRC6XY/Lkyahduza+\n//57seOoRSaTIScnBxcuXBA7SqERFRWFLVu24Pvvv4ednZ3Ycd6Lx5Xpz+bNmxEbG4tJkyaJHeUt\nnp6eGDlypNgxdIremF354sUL5ObmipZl7Nix8PHx0SqDn58fbG1tMXz48I9f/B5HjhzByZMnlY73\n7dsXEyZMgFwuVzoXEhKCdevWmf5rbXVmAxjbh2df6teWLVsIAO3YsUPsKGp7+fIlSSQSmjFjhthR\nCo1hw4aRhYUFPXr0SOwoH6RQKKh69er09ddfix2lUMnNzaU6deqQm5sbKRQKseO8ZdCgQVS2bFmj\ny6WtlJQU6tu3LwGgNm3aUExMjGhZzp8/TwBo6dKlGrcNCwsjQRBo8uTJBcrQuHFjat++vUZt8mZi\nR0REFKhvfYGasy9FL7C0+XBRpj+JiYlUvXp1atq0qdEvxvcuV1dXat26tdgxCoUnT56QpaUlDRky\nROwoahkwYACVKVPG5P7OGrO8hVp3794tdhQlecsj3L9/X+woBRYdHU0ODg4kkUjo559/1vtisB/z\n1VdfUZkyZSglJUXjtoMGDaLixYvT8+fPC5Th/Pnz7/2zjYqKotu3bysdDw4OJgC0a9euAvWtL+oW\nZfz6kuXLzc1F9+7d8fTpUyxfvtzkFuPLG1fGe+MV3KJFiyCXy+Hr6yt2FLV4eXkhISFB63Ew7G1E\nhIULF6Ju3bro3Lmz2HGUFKZFZKdPn46HDx/i2LFjmD59usFmV6py8+ZN7Nu3DyNHjoSNjY1GbR89\nepQ/3KGgsyCbNWuGOnXqqDzn4+ODCRMmKB1v3LgxLC0tcfny5QL1LTbT+q7L9IaIMGrUKBw9ehSr\nV6/WaG0ZY+Hp6YmsrCweV1ZAz58/x5o1a9C3b1/UrFlT7DhqyRtXduLECZGTFA4nT57E5cuXMX78\neFGLhPdp2LAhrK2tcenSJbGjFMjdu3exceNGDBs2LP/vsJguX76M0qVLazVeb8mSJQCgk0lBqamp\nWL16Na5du6Z0ztXVFcHBwUpjxywtLeHs7KxydqZJUedxmrF9+PWl7gUEBBAAmjBhgthRtJaYmJi/\nHxzT3oQJE0gikVBYWJjYUTRSp04d6tixo9gxCoX27dtTxYoVKSMjQ+wo77Vjxw4KCQkRO0aBdOvW\njWxsbAr8uk+XtNkZ5fnz52RlZUUDBgzQSYa0tLT3jhFevnw5AaAnT54onRs+fDjZ2toa5TAG8OtL\npq4DBw5g3Lhx6Ny5M+bPny92HK2VKlUKzs7OvF5ZAcTHx2PlypXo0aMH6tWrJ3YcjXh7e+PUqVOi\nzlorDK5fv47//e9/GD16NIoXLy52nPfq2bOnVutgGZMpU6ZgzZo1RrHo6ePHjwFAqyWQli9fjszM\nTJ3N0rW2tkaDBg1ULn8hlUoBQOUTMalUiuTkZNy/f18nOcTARVkRFxoaip49e6JJkybYtm2byY0j\ne5enpyfOnz+PrKwssaOYpICAAKSlpWHq1KliR9GYl5cXkpOTVb7yYOpbtGgRbGxsMHToULGjfFBa\nWhr27t2LqKgosaNorWnTpujdu7fYMRAbG4t69eph4cKFWrUfPnw4Nm7ciPr16+ssk4uLi8qizNnZ\nGWZmZirPubm5AYBJjysz7e/ArECePXuGL7/8Era2tti3b59JLBL7MTKZDJmZmSY/1kQMcXFxWL58\nObp27YqGDRuKHUdjnp6eAHi9soKIjIzEzp078cMPPxjdPqfvSkxMxNdff419+/aJHUVjJ06cQP/+\n/REfHy92FADAsmXLkJWVhU6dOmnV3t7eHt9++61OM7m6uiImJgbPnj1767i1tTW2b9+Ofv36KbWp\nX78+GjZsaNJPy7koK6LS09PRqVMnxMfHY//+/ahcubLYkXSidevWEASBX2FqKCMjA506dUJWVhZm\nzpwpdhytVKpUCY6OjjzYvwD8/f0hkUgwZswYsaN8VJUqVWBvb2+SMzDPnj2L06dPG8UPwsnJyQgM\nDMTXX38NBwcHjdqmp6fj66+/1suTKVdXVwCqNyDv3r076tatq3Tc3NwcN2/exIABA3Sex1C4KCuC\nFAoFvv32WwQHB2P79u1wcXERO5LOlClTBo0bN1a5GjRTLe/vw7lz57B161Y0btxY7Eha8/Lywtmz\nZ5GTkyN2FJMTHx+P3377DX369EGVKlXEjqMWd3d3kyzKZsyYgRs3bog+Zi8nJwcTJkxAUlKSVlsU\nXbt2DUeOHEFGRobOszVp0uS9G5DHxcVh27ZtiIuLe297ItNc2Z+LsiJo+vTp2L17NxYuXKj142pj\nJpPJcO7cOWRnZ4sdxSRMmjQJf/75JxYvXoxvvvlG7DgF4uXlhbS0NJMeUyKWwMBApKenq1wDylh5\neHggPDzcaF4Dfkxubi5u374NwDg20Y6Pj8fevXsxZsyY/CdTmmjRogWePHmCVq1a6TybjY0NHBwc\ncPXqVaVzDx48QL9+/XD27Fmlc6dPn0blypVNds1CLsqKmM2bN2PevHkYPHgwfvrpJ7Hj6IWnpycy\nMjJMf70aAwgMDMTixYvx448/Yty4cWLHKbC8cWX8ClMz6enp+PXXX9GxY0c4OjqKHUdteYvImsoY\n0q1bt6JRo0ai5z1y5AjkcjkqVaqEmzdvwt/fX+N7xMTEgIhQqlQpCIKgh5Tv34DcyckJZmZmKr/G\nV65cGQ4ODqY72UuddTOM7cPrlGnn9OnTZGFhQd7e3pSdnS12HL158eIFAaBffvlF7ChGbd++fSSR\nSKhjx46ib+2iS87OzuTt7S12DJOQlZVFGzdupIYNGxIAOnPmjNiRNJKenk5Xrlwxia9nGRkZVLVq\nVVH3Ek1JSaGBAwcSAFq/fr3W98nNzSUHBwfq37+/DtMpW7JkCVlaWlJ8fLzSOWdnZ/r888/12r8u\ngfe+ZG+6f/8+lS1blurVq0cJCQlix9G7hg0bUrt27cSOYbQuXbpE1tbWJJVKtVos0piNGTOGihcv\nbtQLn4otMTGRFixYQPb29gSAGjduTDt37hQ7VqHm7+9PAOjYsWOi9H/16lVycHAgQRBo2rRplJOT\no/W98vZF/eOPP3SYUFlaWtp7C+6PbUqflZWlz2ga46KM5UtISCAHBwcqU6ZModjAVx0//vgjlShR\nwiR+gja0iIgIqlChAtWoUYNiYmLEjqNz+/btIwAUFBQkdhSj8+jRIxo3bhyVLFmSAFCbNm3of//7\nn2hPbnThwoULNGHCBKP+PSQlJVG5cuWoTZs2ovT/22+/kaWlJdnb29OJEycKdC+FQkFNmzalunXr\nivqEfeXKlQSAIiMjlc75+/uTtbU1ZWZmipBMNXWLMh5TVsjl5OSgW7duiIiIwN69e9+7yWthI5PJ\nkJaWpnI8QlGWkJAAHx8f5OTk4ODBg6hYsaLYkXSudevWkEgkvF7ZG0JCQtC3b1/UqlULy5YtQ8eO\nHXH16lUcPXoUn3/+ud7GBBlCaGgoFi1ahPDwcLGjvNfSpUsRFxeHefPmidJ/lSpV0L59e1y/fr3A\ne2weOXIE165dg6+vr0H2RZ0/f77Kxay7deuGu3fvomrVqkrnqlativT0dISGhuo9n86pU7kZ24ef\nlKlHoVDQ999/TwBo06ZNYscxqJiYGAJA8+fPFzuK0cjMzKTWrVuTpaUlnTp1Suw4eiWVSqlVq1Zi\nxxCVQqGgw4cPU5s2bQgA2djY0NixYykqKkrsaDp1/fp1AkDbtm0TO4pKsbGxZGNjQ926dTNov8eP\nH6dly5bp/L6tW7emKlWqGOz1YK9evTT+txwZGUkAKDAwUE+pNAd+fckWL15MAGjy5MliRxFFgwYN\nqH379mLHMApyuZx69uxJAGj79u1ix9G7iRMnkoWFBaWlpYkdxeCys7Npy5Yt5OTkRADIzs6O/Pz8\nCu1Y0tzcXCpRogSNHDlS7CgqjRkzhiQSCd25c8cg/WVnZ9OUKVNIEARq2LChTl/hnTlzhgBQQECA\nzu75MR8a+/bXX3/R0qVLlY4rFAoqX748DRw4UJ/RNGLQogxAewBhAMIB+Ko4LwBY/vp8KAAXdduq\n+nBR9nF///03CYJA3bp1I7lcLnYcUQwdOpRsbGwKNKC1sPD19SUA5OfnJ3YUgzh06BABoCNHjogd\nxWCSkpJo8eLFVKVKFQJAjo6OtGHDBqMaV6MvMpmM3N3dxY6hJDIykiwtLem7774zSH8PHz6kZs2a\nEQD67rvvdD6Jx8fHh8qVK2c0k4O+//57Kl26tMrxhD4+PtSoUSMRUqlmsKIMgBmABwBqAbAEcB2A\n4zvX+AA49Lo4awbgorptVX24KPuwq1evkrW1Nbm5uRXJJwV5duzYQQDo0qVLYkcR1Zo1awgADRky\nxKgHQ+tSSkoKmZubF4mnxE+ePKEJEyaQra0tASBPT086cOBAkfphbOLEiVSuXDmj+wHs+fPnNGLE\nCHr06JHe+4qPj6fSpUuTra2tXmbSXrt2jQDQ3LlzdX7vD8nNzaXOnTvTqlWrlM6tXr2aAFBERITS\nuRkzZpBEIjGaAtKQRdmnAA6/8evJACa/c80aAL3e+HUYADt12qr6cFH2fk+ePKHKlStT1apVKTo6\nWuw4ooqOjiYAtHDhQrGjiObAgQNkZmZGPj4+RvcNS9+aN29OHh4eYsfQm9DQUOrfvz+Zm5uTRCKh\nHj160OXLl8WOJYrU1NRCtdaeJt78fa9du1ZlgaIL+/fvJwcHB0pMTNTL/T+kTp061LVrV6XjwcHB\nBIB27dqldG7//v1GtfaeukWZLmZfVgbw+I1fP3l9TJ1r1GkLABAEYYggCMGCIAS/ePGiwKELo7S0\nNHz11VdISkrC/v37YWdnJ3YkUdnZ2aFevXpFdnPyq1evonv37nBycsIff/wBc3NzsSMZlJeXF4KD\ng5GSkiJ2FJ0hIhw/fhwdOnSAk5MTdu/ejeHDhyM8PBw7d+6EVCoVO6IoSpQoYZCZgJqYOXMm/vvv\nP732cf36dTg7O+fv9fv999+jZs2aeunryy+/xJ07d1CqVCm93P9DXFxcVM6kb9SoESwsLFSey/u3\nYGpbrpnMkhhEtJaIpEQkLV++vNhxjI5CoUC/fv0QEhKCHTt2wNnZWexIRkEmk+HMmTOQy+ViRzGo\nqKgofPHFFyhbtiz+/fdf2NjYiB3J4Ly8vCCXy3HmzBmxoxRYTk4Otm/fDldXV7Rp0wbXrl3D3Llz\n8fjxYyxbtkxv34hNybRp0zB9+nSxYwAAnj17hiVLluhtWRYiwooVK+Dh4YGEhAS9L2ly/vx55OTk\niLZ0iqurKyIjI5X2OC1WrBicnJxw7949pTaVKlVClSpVTG67PV0UZU8BvLlQSJXXx9S5Rp22TA1T\npkzB3r17sWTJEnz55ZdixzEanp6eSE5ORkhIiNhRDObly5fw8fFBRkYGDh48CHt7e7EjiaJ58+aw\ntLQ06X0wU1JSEBAQgDp16qBPnz5IT0/HunXrEBkZialTp6JMmTJiRzQat2/fxo4dO8SOAeDVU/rw\n8HCMGTNG5/eOj49H586dMXLkSHz22We4fv06ZDKZzvvJ8/jxY8hkMsyaNUtvfXxM3mbp165dUzp3\n/Phx7NmzR2W76dOn45tvvtFrNp1T5x3nhz4AzAFEAKiJ/x+s3/Cda77A2wP9L6nbVtWHx5S9bcOG\nDQSAhg4dWmQGcqvryZMnBICWLFkidhSDyMrKIi8vL7KwsKDjx4+LHUd0MpmMXFxcxI6hsejoaPL1\n9aVSpUoRAGrVqhXt27evSA3e19T8+fMJAL148ULUHHFxcXr9Ojx//nyysLAgf39/g3y9z8jIoJUr\nV6pcOd9Q4uPjTX7dSRh4SQwfAPfwaibl1NfHhgIY+vq/BQCBr8/fACD9UNuPfbgo+39BQUFkbm5O\nbdu25S2F3qNOnTr01VdfiR1D7xQKBfXr148A0JYtW8SOYxRmz55NgiCYzBpdt27dooEDB5KlpSVJ\nJBLq1q0bXbhwQexYJiEoKIgA0IEDB0TLIJfLycXFhXr16qXT++bk5NC9e/fy//vmzZs6vb8pqFmz\nJn3zzTdKx1+8eEF9+vShQ4cOKZ3Lzc2l0NBQevr0qSEifpBBizJDf7goeyUsLIxKly5NDRo0EGVG\njKn47rvvqFSpUoV+dtb06dMJAP38889iRzEap0+fJgC0d+9esaN8UGpqKnXu3JkAkJWVFf34448U\nHh4udiyTkpKSQhKJhGbMmCFahj/++IMA0ObNm3V2z8jISGrevDlVqlSJkpOTdXZfdSxfvpxWr15t\n0D7fp2vXrlSrVi2l41lZWWRpaUnjx49XOhcXF2c0T9jULcpMZqA/e1tCQgK+/PJLmJmZ4d9//xVl\nRoypkMlkePnyJW7cuCF2FL3ZsGED5syZg0GDBmHatGlixzEa7u7usLKyMup9MIkIgwcPxr59+zBz\n5kw8evQIK1asQO3atcWOZlJsbGzg6ekpWv85OTmYNm0aGjZsiD59+ujknrt370aTJk1w48YNLF26\nFCVLltTJfdWRnJyM6dOn4+jRowbr80NcXV0RERGBxMTEt45bWlrCyclJ5QzMsmXLYuXKlfDx8TFU\nzIJTp3Iztk9Rf1KWlZVFMpmMLC0t6ezZs2LHMXpRUVEEgPz9/cWOoheHDx8mMzMzateuHb/CVqFt\n27ZGtbL3u5YuXVqkdlsorNauXUsA6J9//inwvTIyMmjIkCEEgNzd3enBgwc6SKiZvDF6wcHBBu9b\nldOnT9MXX3xBDx8+VDo3dOhQsrW1Neoxl+DXl4WTQqGggQMHGvUGvMaoZs2a1LlzZ7Fj6FxISAiV\nLFmSnJycKCkpSew4RmnevHkEgGJjY8WOoiQoKIjMzMyoS5cuPEnHhKWnp5O9vT19+umnOvlzzM3N\nJU9PT5o0aZIoP2ilp6dThQoV6PPPPzd439r47bffCED+uLs3vXz5kvbv308vX74UIdn/U7co49eX\nJmbhwoXYuHEjZsyYobNH5EWBTCbD6dOnoVAoxI6iM0+ePMEXX3wBW1tbHDhwALa2tmJHMkpeXl4A\nkL/AprF48uQJevTogbp162LTpk2irQFVmDx9+hT169fH77//btB+V6xYgejoaPj5+Wn950hEWLdu\nHWJiYmBmZoajR49i/vz5sLCw0HHaj9uwYQNiY2MxZcoUg/f9MdnZ2UrHpFIpatasiefPnyudCwkJ\nQceOHXHu3DlDxCs4dSo3Y/sU1Sdle/bsIQDUo0cP/qlaQ5s2bSIAdP36dbGj6ERSUhI5OTlRyZIl\nC83vSV9ycnKoZMmSNHToULGj5MvMzCQPDw+ysbGh27dvix2n0MjNzaUSJUrQiBEjDNZnYmIilS5d\nmtq3b6/1PeLj46lLly4EQNSJCkRE2dnZVK1aNWrRooXRfZ8ZNmwYOTg4aNQmOTmZBEGg2bNn6ymV\neqDmk7Kite+KCQsODkbfvn3RrFkzbNy4kX+q1lDe4oqnTp2Ck5OTyGkKJicnB926dcPt27dx8OBB\nk//96Ju5uTlatWplVIP9R48ejYsXL2LPnj1o0KCB2HEKDTMzM0ilUly8eNFgfS5evBiJiYmYN2+e\nVu0jIyPRunVrxMTEYMmSJXpZcFYT27dvx6NHj7By5Uqj+z7j4eEBiUQChUIBiUS9F30lS5ZE/fr1\nTWdlf3UqN2P7FLUnZY8fPyY7OzuqXr06xcTEiB3HZFWrVk3lpram5M0xhRs2bBA7jslYtGgRATCK\n9YrWr19PAMjX11fsKIXSxIkTycLCgjIyMgzSX2BgIA0fPlzr9j179iRra2uj2Ew+NzeXHBwcyMnJ\nyeiekn3MmjVryNHRUeVg/379+lGlSpVE/T2Bx5QVDqmpqejYsSNSU1Px77//omLFimJHMlmenp44\ndeoUXv37ME1z587NH1M4cOBAseOYDG9vbwAQ/WlZcHAwhg8fjjZt2mDu3LmiZimsPDw8kJOTY7Ct\n1YYPH47AwECt2qanp+PGjRsYO3asUWwmf+jQIYSFhWHKlClG95QsT25urtKyGABgYWGB27dv4/79\n+0rn3NzcEBMTg+joaENELBAuyoyYXC5Hnz59EBoail27dqFRo0ZiRzJpMpkMcXFxuH37tthRtLJl\nyxbMmDED/fv3F3UfOlPk7OyMUqVKiVqUvXjxAl9//TUqVqyIHTt2wMzMTLQshVmzZs3QtWtXvQ+Q\nfyizxBgAACAASURBVPjwIbZs2QK5XK71PaytrRESEoKpU6fqMJn2fHx8sG/fPnTr1k3sKO/l6OiI\nESNGKB3PK2pVrVeWd+7y5cv6DacDXJQZsUmTJmHfvn1YtmwZ2rdvL3Yck/fmuDJTc+LECXz33Xfw\n9vbGunXrjPanWGNlZmYGmUwmWlGWm5uLXr16ITY2Fn/99RfKlSsnSo6iwN7eHrt3787fxFpfli5d\nih9++AGxsbFatb937x6Sk5Nhbm4OKysrHafTjkQiQceOHY36B4YGDRrg6tWrKo9bWVmpHDvWpEkT\nmJmZmcS4Mi7KjNS6deuwZMkSjBgxQuVPBUxztWrVQpUqVYxuaYSPuXnzJr7++ms4ODhgz549sLS0\nFDuSSfL29kZERASioqIM3vfUqVNx/PhxrF69Wu/FAnslLi5Or/dftGgRjhw5Ajs7O43bKhQK9OrV\nC5999pnRDKfo2rUrVqxYIXaMj3J1dUVYWBhSUlLeOm5ubo4mTZqofFJmZWWFRo0a8ZMypp3jx49j\n+PDh6NChA/z9/cWOU2gIggCZTGZS48qio6Ph4+MDa2trHDx4kLfTKoC89coM/bRsz549WLhwIYYO\nHYoBAwYYtO+iKjAwEOXLl9dbYZabm4vixYujVatWWrXftWsXrl69ilGjRhnFU++kpCRERUUV6FWs\nobi4uICIVI4Z7NSp03uH+bi5uSE4ONj4v/arMxvA2D6FefZlREQElSlThho2bMgrtOvBunXrCADd\nuXNH7CgflZycTE2bNiUbGxu6evWq2HFMnlwup3LlylH//v0N1uetW7fIxsaGmjVrRpmZmQbrt6g7\nefIkAaADBw7o/N5nz56lGjVqaL0+YFZWFtWqVYucnJwoNzdXx+m0p1AoKCcnR+wYHxUdHU0AKCAg\nQKN2UVFR9OzZMz2l+jjw7EvTk5GRga+//hoKhQL//PMPr9CuB3njyoz9FWZubi66d++eP8mjadOm\nYkcyeRKJBF5eXjhx4oRBflpOSkpCly5dYG1tjd27d6NYsWJ675O94urqColEovP1yogIvr6+yMzM\n1HrD+LVr1yIiIgLz5883irFbjx8/RkJCAgRBgLm58S9damdnBzs7O5WvKYFXf0YZGRlKx6tVq4ZK\nlSrpO16BcVFmJIgIw4YNQ0hICLZt26b1P3j2YXXq1IGdnZ1RD/YnIvz444/43//+h5UrV6JDhw5i\nRyo0vLy88OTJEzx48ECv/SgUCgwYMAAPHjzAn3/+icqVK+u1P/Y2GxsbNGzYUOdF2aFDh3D27FlM\nnz4dJUqU0Ooex44dg6enp1FM3jp9+jSaN28OJycn5OTkiB1HbS4uLiqLMiJCjRo1MHnyZJXtli1b\nhm3btuk7XsGo8zjN2D6F8fXlypUrCQDNmjVL7CiFXq9evURfSPBD/Pz8CABNnjxZ7CiFzp07dwgA\nrV27Vq/95G2C7u/vr9d+2PsNHjyYSpcurbN/53K5nJydnalWrVqUlZWl9X0UCgUlJCToJJO2srKy\nyNfXlwRBoDp16tDFixdFzaOpGTNmkEQiodTUVKVzzZs3pxYtWqhs5+7uTm3bttV3PJWg5utL0Qss\nbT6FrSg7d+4cWVhY0BdffKFyNWKmW6tXryYAFBYWJnYUJb///jsBoN69e/PfBT1QKBRkZ2dHPXv2\n1Fsfhw8fJkEQqFevXkZb+BcFQUFBtHz58gIVUG/avn07AaDff/9dq/ZxcXFGsSPLnTt3yNXVlQDQ\nd999RykpKWJH0tg///xDAOi///5TOjdq1CiytrZWOV4vOjpatHFzXJSZiGfPnpG9vT3Vrl1b9J+e\nigpDPS3RhEKhoICAAJJIJNS6dWseFK5HvXv3pooVK+qlYMqbqNO4cWOVP8Uz0/Tm4Hxtf1gaOXIk\nlSpVil6+fKnjdOpRKBS0atUqsrKyorJly9Jff/0lSg5diI2NpV9//VXltmmbN28mAHTz5k0Rkr2f\nukUZjykTUU5ODrp3747ExET89ddfKF26tNiRigQHBwdUrFjRaAb75+TkYOjQoRgzZgw6duyIAwcO\n8KBwPfL29sbz589x9+5dnd73zYk6e/fu1XrMEdOdqKgolQuNamr9+vWIiIiAn5+f2hthv+nBgwdY\nvXo1evTogU8++aTAeTQVGxuLTp06YdiwYWjVqhVCQ0PRpUsXg+fQlfLly2PEiBGwt7dXOvehlf2T\nk5Pzx+saLXUqN2P7FJYnZWPGjCnQ43Cmve7du1PlypVFf70UFxdHnp6e+WPI+JWl/j148IAA0IoV\nK3R2T4VCQf379ycA9O+//+rsvqxgPD09SSqVFugeaWlpVKlSJWrVqpXWXy969epF1tbWFB0dXaAs\n2tq/fz8VK1aMAgICCs3XmMePH9PBgweVjufm5tKIESPo3LlzKs9ZW1vTqFGjDBHxLeDXl8Ytb3zC\n6NGjxY5SJAUGBhIACg8PFy3DrVu3qFatWlSs2P+xd+/xPdf//8dvzzZzyplETsPIaRsbs0RzTOUs\nSUo6qI9CSjmkcxJSks/6+PVxlmMhPlJEyCHlfCjLkBxCch5mp+fvj82+Y+9l59d72/16ubwv3u/3\n6/l+ve6vbebh+Xq+ns/8dubMmY7lyGvi4uJspUqVbNeuXTNtn9d+nnSjjnsZMmSIzZcvn71y5Uq6\n97F3715bq1Ytu379+nR9ftu2bRawr776arozpMfly5ftihUrEl+7utSXk7322mvWw8PDXr58OU2f\nu/vuu21wcHAWpUqZijI3tnPnTluwYEHbtGlTGxUV5XScPOmXX36xgJ00aZIjx1+2bJktWrSoLVu2\nrP3xxx8dyZCX9e7d25YqVSpTeg02bNhgPT09bbt27XJNL0RusXDhQgu47DVJi4x8X998801bqlSp\nbB9L9vjjj1svL69cV4xd8+uvv9olS5a4HH8bExNj9+zZ43JQ/4svvmgLFCiQ7QP+U1uUZWhMmTGm\npDHmO2NMeMKfLgdFGWPaGmN+M8bsN8YMTfJ+N2PML8aYOGNMYEay5BTnzp2jS5cuFC9enPnz55Mv\nXz6nI+VJtWrVokyZMtk+X5m1lnHjxtGuXTuqVq3Kzz//TOPGjbM1g8TPV3b69Gl2796dof2cOHGC\nBx98kMqVKzNz5sx0jTeSrBMUFATAzz//nK7Pr1q1iosXL2bo+/rWW2+xe/fubBlLFhcXl7gm5Btv\nvMHSpUtdjrvKDWrVqkX79u1djr+dO3cudevWdTluNDAwkMjISH755ZfsiJlmGf0NMhRYZa31AVYl\nvL6OMcYDCAXuA2oDPYwxtRM27wG6AD9kMEeOEBcXx2OPPcbhw4f58ssvc8TswrmVMYZmzZpl6zqY\nUVFR9OnTh5deeomOHTuyfv16KlWqlC3HlutlxjqY0dHRdOvWjfPnz7Nw4UKtS+qGypcvT4UKFdI1\niezJkydp3749r7zySrqOHRcXx+HDhwHStWh5Wh05coSWLVvy6KOPYq2latWqtG7dOsuP66SNGzey\nfPnyZO8HBAQAsGXLlmTbGjZsmOI2t5Ca7rSUHsBvQLmE5+WA31y0CQaWJ3k9DBh2Q5s1QGBqj5tT\nL1++/fbbFrChoaFORxFr7YQJEyxgDx48mOXHOnXqlG3WrJkF7GuvvabLXG6gevXqtn379un+/IAB\nAyxgZ8+enYmpJLOtWLEiXWvdxsXF2YULF6b798O8efNsvnz5smVi1rlz59rixYvbwoUL28mTJzt+\nA1N2adOmjfX390/2fmxsrL311lvt888/73JbsWLF7LPPPpsdERORHWPKgHNJnpukr5O8/yAwKcnr\nx4B/39DmpkUZ8AywBdhSqVKlrPq6ZZmvv/7aGmNsr1698sxfGHe3a9cuC9ipU6dm6XH27Nljvb29\nbf78+fUPuBvp06ePLVasWLoWhZ45c6YF7IsvvpgFySSni4qKstWqVbP16tXL0kXHz58/bx977DEL\n2KCgIBseHp5lx3JHw4YNs56eni5v5GjWrJlt3Lixy8+1bNnSZnfnTmqLsptevjTGrDTG7HHx6HhD\nj5sFsuw6kLX2M2ttoLU2sEyZMll1mCxx4MABevbsiZ+fHxMnTsQY43QkAerUqUPJkiWzdL6yr7/+\nmuDgYK5cucIPP/xAjx49suxYkjbNmzfn/PnzbN++PU2f27lzJ8888wzNmjVj9OjRWZROMsvly5eZ\nPn16msYPPvPMM4wZMybdx/zvf//LgQMHeP/997Ns0fENGzbg7+/PrFmzeOONN1i3bh3Vq1fPkmO5\nqwYNGhATE8OePXuSbQsMDGTHjh3ExMS43LZr1y6uXr2aHTHT5KZFmbW2lbW2rovHYuCkMaYcQMKf\nf7nYxTGgYpLXFRLeyxMuX75Mly5dMMawcOFCChYs6HQkSXDLLbdwzz33ZMlgf2stY8eOpX379vj4\n+LB582YaNWqU6ceR9Ls2ruz7779P9WfOnDlD586dKVGihG7UySGstTz55JN88cUXqWq/fft2/vvf\n/3L+/Pl0HS8iIoJ33nmHZs2acf/996drH/8kOjqaN954g2bNmgGwbt063n777Tz5s3ht7JiriWJ7\n9uzJ1KlTiY2NTbYtODiY2rVrc+LEiSzPmGap6U5L6QF8AAxNeD4UGOOijSdwEPAGvICdQJ0b2qwh\nF44pi4uLs4888og1xthvv/3W6Tjiwscff2wBe+jQoUzbZ2RkpH3iiScsYB988EEtt+PGatWqZdu2\nbZuqtjExMbZt27Y2X758msYkh/H19bVt2rRJVdv77rvPlihRwp49ezZdx1q4cKE1xmTZz8gnn3xi\nAdurVy97/vz5LDlGThEXF2dLlChh+/Tp43SUmyKbxpSVIv6uy3BgJVAy4f3ywLIk7e4H9gEHgOFJ\n3u8MHAWuAidJckPAPz1ySlE2fvx4C9gRI0Y4HUVSsGPHDgvY6dOnZ8r+Tp48ae+++24L2DfeeEMD\n+t3cc889ZwsXLpyq+QJff/11C9iJEydmQzLJTH369LHFixe/6d/HNWvWWMCOGTMmQ8fL7JuH4uLi\n7PHjx6218etwatWI/9OqVasUx4dt27bNrl27NpsTuZYtRZlTj5xQlK1bt856enraDh066B9mNxYb\nG2tLlChhn3zyyQzva9euXbZy5cq2QIECdu7cuZmQTrLal19+aQG7YcOGf2y3ePFiC9gnnnhCN+rk\nQJMmTbKADQsLS7FNXFycDQ4OtuXLl0/zLPHXnDhxIr0R/1GfPn1sxYoVHVvM3J0NHjzYenl52atX\nrybbFhISYhs1auTyc8OGDbNNmjTJ6niJUluUaabDLPDnn3/SrVs3vL29mTFjhiaUdGO33HILTZs2\nzfC4sv/973/cddddREdHs27dOrp3755JCSUr3XPPPcA/z1cWHh7OY489RkBAAKGhobpRJwe6Nons\n5s2bU2yzdOlSfvzxR9588810jf09ePAglStXZsqUKenOmZIHHniAAQMGUKRIkUzfd04XEBBAVFRU\nioP9d+7cSVRUVLJtZcuWpWzZssTFxWVHzNRLTeXmbg937im7evWqveuuu2zhwoXtnj17nI4jqfDR\nRx9ZwB45ciTNn42Li7OjR4+2xhgbGBhojx49mgUJJSv5+vrali1butx28eJFW6dOHVuqVKlMHXco\n2SsmJsbu27cvxV7OmJgYW7duXevj45Pupe8eeeQRW7BgwUz5HXDlyhU7cOBA+8EHH2R4X7nd/v37\nU1wyb86cORaw27ZtcyDZ9VBPmTMGDRrExo0bmTJlCnXq1HE6jqTCtd6StPaWXb16ld69ezNkyBC6\ndevG2rVrueOOO7IiomShFi1asGHDhmS3x1treeqpp9i7dy9z586lcuXKDiWUjPLw8MDHxyfFXs7Z\ns2ezZ88eRowYka67GLdv387s2bMZOHBghn8H7N69m0aNGvHxxx9z/PjxDO0rL6hatSrh4eE8+eST\nybYFBsav3ujq7sxrXE2Z4ajUVG7u9nDXnrIZM2ZYwA4aNMjpKJIGMTExtlixYmm6g+fEiRM2ODjY\nAvbtt9/WOKMc7Np4sTVr1lz3/tixYy1gR40a5VAyyUybNm2yTz75pMvxYn369LENGjRI9/jfe++9\n15YsWTLdd2xaGz++9aOPPrJeXl62bNmydtmyZenel8SLi4v7x9n7mzVrZh955JFsyYIG+mevbdu2\n2QIFCtiQkJBsX31eMq5du3bWx8cnVW137NhhK1WqZAsWLGjnz5+fxckkq509e9becsst9s0330x8\n7/vvv7ceHh62a9euKrhziYULF1rAbty40eX29E4vER4ebj09Pe3YsWPTne3YsWO2devWFrDt27e3\nJ0+eTPe+8qINGzbYp556yuWl5/Xr1yfeuXqjzp07p/r3fkaltijT5ctMcObMGbp27UqpUqWYN28e\nnp6eTkeSNLrnnnsIDw+/6eWCr776iiZNmhAbG8u6devo1q1bNiWUrFK8eHEaNGiQOInskSNH6N69\nOz4+PkydOlUD+3OJa4P9U1qcvGjRounab/Xq1QkLC+P5559P1+fDwsLw9fVl/fr1TJw4kcWLF3Pb\nbbela1951R9//MG0adM4cOBAsm1NmjTh9ttvd/m5wMBAwsPDOXfuXFZHTDUVZRkUGxvLI488wrFj\nx1iwYIH+MuVQNxtXZq3l/fffp0uXLtSuXZvNmzcnziYtOV/z5s3ZtGkTZ8+epWvXrkRGRrJo0SLd\n7ZaLlC9fngoVKqRYlKXHtX/Mq1WrRoECBdK1j5dffpmYmBi2b9/Os88+q/8EpEOnTp24ePEid955\nZ7Jtf/31Fx9++KHLgq1hw4YULVrU5TanqCjLoLfeeovly5czYcKExP+JSc5Tv359ihQp4nIdzMjI\nSHr16sWrr77Kww8/zNq1aylXrlz2h5Qs06JFC6Kjo2ndujWbN29m+vTpLn/BS84WFBSUaUVZdHQ0\njRo14qWXXkr3Ps6dO8eBAwd49dVXqVmzZqbkyosKFiyY4jQmERERvPzyy6xatSrZthYtWnD27Fm3\n+g+2irIMWLJkCSNGjODJJ5+kT58+TseRDPD09OTuu+9O1lN24sQJmjdvzueff86IESOYNWuW1i/N\nhe6++248PT3ZunUrw4YNo3Pnzk5HkiwQFBREZGQkERERGd7X5MmTCQ8Pp0WLFuneR/Hixdm9ezcD\nBw7McJ687rPPPmPAgAHJ3vf29qZEiRJs2bIl2TYPDw+3m0fUxI8/y1kCAwOtqy9wdgoPDycwMJAa\nNWqwbt26dHddi/sYPXo0Q4cO5fjx49x+++1s376djh07cvr0aWbMmEHXrl2djihZqGPHjlhrWbRo\nER4eHk7HkSwQExODh4dHhi8RRkREUL16dWrUqMHatWvTtb9du3ZRpUqVdI9lk+u99NJLTJw4kQsX\nLiQb1926dWvOnDnzj1NjZDVjzFZrbeDN2mlEejpERETQuXNn8uXLx5dffqmCLJcICQkB4IcffsDT\n05PHHnuMkiVLsn79eurXr+9sOMlyX331FYDG9ORimXUT1scff8zJkydZtGhRun5eoqKi6Ny5M97e\n3qxcuTJTMuV1AQEBXLlyhbCwMOrWrXvdtsDAQD788EMiIyPd/t9rFWVpZK3l6aefZu/evSxfvlwT\nSuYiDRo0oHDhwgwfPpz9+/cTFBTEV199leKdO5K7qBjLG1577TWOHz/O5MmT0/X5qKgoPv30Uzp1\n6kRwcHC69pEvXz7Gjh1LqVKl0vV5Se7auLCtW7cmK8oCAgKIjo4mLCwMf39/J+KlmntdTM0Bxo0b\nx7x58xg5ciStWrVyOo5konz58nH33Xezf/9+evbsyZo1a1SQieQyp06dYuHChele89DLy4vt27cz\nfvz4dGcwxtC5c2eaNWuW7n3I9Xx8fChcuLDLS5T3338/Z86ccfuCDFSUpcmaNWsYPHgwXbp0YfDg\nwU7HkSwwZswYPv/8c2bOnOn23dwiknZBQUGcO3eO8PDwNH/20qVLWGspW7YslSpVStfx33vvPd55\n5x1y4nhud+bh4UH9+vVdFmWFChWiRIkSDqRKOxVlqXT06FEeeughTSiZy/n6+tKzZ099f0VyqZtN\nIvtP/vWvf9G6det0F1RHjx5lxIgR7Nu3T79jskCDBg3YsWMHsbGxybbNmTMnR9zlqqIsFa5evcqD\nDz7IlStXWLRoke6WERHJoe68806KFCmS5qJs586dzJo1i8DAwHQXVG+88QZxcXGMGDEiXZ+XfxYQ\nEMDly5f57bffkm3bs2cPoaGhREZGOpAs9VSUpcLAgQP56aefNKGkiEgO5+HhQefOnSlZsmSaPjds\n2DCKFy/OkCFD0nXc3bt3M336dPr160eVKlXStQ/5Z0kH+98oMDCQmJgYdu3ald2x0kR3X97E1KlT\nmThxIkOGDKFLly5OxxERkQyaPn16mtqvWbOGb775hjFjxqR7bNLQoUMpWrQow4cPT9fn5eZq1qxJ\nvXr1XN7Eca1g27JlC40aNcruaKmmouwfbN26lb59+9KqVSt1N4uI5CLWWmJjY1M1d9mYMWOoUKEC\n/fr1S9exVq9ezbJlyxgzZkyae+gk9Tw9PVPsCatYsSJlypRxdALZ1NDlyxT8/fffdOnShbJlyzJn\nzpxMm3RQREScdeHCBe644w4mTJiQqvZz5szhq6++StcSa3FxcQwePJhKlSrRv3//NH9e0ufGmzGM\nMQQFBXHhwgWHEqWOKg0XYmNj6dGjBydPnmT9+vWULl3a6UgiIpJJihYtiqen500H+8fExGCMoVix\nYuletHr+/Pls2bKF6dOna5qdbPDdd9/Rq1cvVq9enWwM+OLFi91urcsbuXc6h4wZM4aVK1fy6aef\nEhh406WqREQkhwkKCrppUTZ58mT8/Pz466+/0n2cNWvW4OfnR8+ePdO9D0m9O+64gzp16nDlypVk\n29y9IAMtSO7SqVOnmDt3rrqaRURyqbFjx/LKK69w8uRJbrvttmTbL126RPXq1alWrRrr1q3L0Lxi\n58+fp1ixYhmJK5kgIiKCTp068cgjj/Dkk09m67FTuyB5hspGY0xJY8x3xpjwhD9d3pZijGlrjPnN\nGLPfGDM0yfsfGGPCjDG7jDGLjDHFM5Ins5QpU0YFmYhILnZtEtmff/7Z5faPP/6YEydOMHr06HQV\nZOfPn+f3338HUEHmgOjo6GTvFS5cmN27d/PDDz84kCh1MtqXNxRYZa31AVYlvL6OMcYDCAXuA2oD\nPYwxtRM2fwfUtdb6AvuAYRnMIyIiclMBAQH07t3bZS/Z33//zejRo+nYsSNNmjRJ1/5HjRpF7dq1\nOXHiREajShq9++673H777cmmxjDGEBgYSFZeacuojA707wiEJDyfDqwBbpxZrxGw31p7EMAYMzfh\nc79aa1ckabcJeDCDeURERG6qUKFCTJ061eW2CRMmcOnSJUaOHJnu/Xfv3p3SpUtz++23p3sfkj7l\nypXjzJkzHDhwAB8fn+u2BQQE8O2333Lp0iUKFy7sUMKUZbSnrKy19njC8xNAWRdt7gCOJHl9NOG9\nGz0JfJPSgYwxzxhjthhjtpw6dSq9eUVERID4aRPCw8OT9agMGzaMZcuWUbt27RQ+eXP+/v4MGjQo\noxElHW42s39cXBw7duzI7lipctOizBiz0hizx8WjY9J2Nv6OgXTdNWCMGQ7EALNSamOt/cxaG2it\nDSxTpkx6DiMiIpJo5syZ1KhRg3379iW+FxsbS4ECBbj33nvTtc9du3bRq1cvTp48mVkxJY3q1KmD\nl5cX27ZtS7YtMDCQoKAgl2PO3MFNizJrbStrbV0Xj8XASWNMOYCEP13dN3wMqJjkdYWE90j4XG+g\nHdDT5sRbQUVEJEe6NuXRtakxdu3aRfXq1TM05mjIkCEsXboULy+vTMkoaefl5UW9evVc9pSVL1+e\nTZs2ERISkv3BUiGjly+XAI8nPH8cWOyizWbAxxjjbYzxAh5O+BzGmLbAYKCDtfZyBrOIiIikWs2a\nNSlSpEhiUTZs2DDOnTtHtWrV0rW/VatW8e233zJ8+PB0r5EpmSMgIIBt27Ylm9n/mtjY2GxOlDoZ\nLcpGAa2NMeFAq4TXGGPKG2OWAVhrY4B+wHJgLzDfWvtLwuf/DRQBvjPG7DDGTMxgHhERkVTx8PCg\nYcOG/PTTT6xdu5Zly5YxbNiwdBVUSZdTev7557MgraRFQEAA586dS5yWJKlJkyZRrFgxIiIiHEj2\nzzJ096W19jTQ0sX7fwL3J3m9DFjmol31jBxfREQkI4KCghgzZgwDBgzgjjvuSPcclXPnzmXbtm3M\nnDlTyym5gQYNGgDxg/2rVq163bbbb7+dS5cusWPHDu6++24n4qXI/dccEBERySLdu3ena9eu7Nq1\ni3feeSddi45fvXqV4cOH4+/vzyOPPJIFKSWt6tWrR758+VwO9r92d6Y7zlemBclFRCTP8vPzY/bs\n2XTp0oWuXbumax+ffvophw4d4rvvvssR6yvmBfnz5+fdd99N7DFLqly5cpQvX97ljQBO09qXIiIi\n6XT27FmqVatGw4YNWb58udNxJJU6duzIvn372Lt3b7YcL7VrX+aanrLo6GiOHj1KZGSk01HkHxQo\nUIAKFSqQL18+p6OIiGTYDz/8wKVLlxg9erTTUeQG0dHR/Prrr1SpUiXZ+qMPPfQQe/bswVqbocXm\nM1uu6Sn7/fffKVKkCKVKlXKrL7D8H2stp0+f5uLFi3h7ezsdR0QkU5w6dQpNau5+fv75Z4KCgliw\nYAFdunRxNEtqe8pyzcXvyMhIFWRuzhhDqVKl1JspIrnCtZUAVJC5J19fXz744AN8fX1dbr9y5Yrb\nrbyQa4oyQAVZDqDvkYjkBjt37uTOO+9McVFzcV6BAgV4+eWXqV7d9exbtWvX5sUXX8zmVP8sVxVl\nIiIi2eH222/nhRdeoFOnTk5HkX/w999/8/XXX7uc2d/f39/t7sBUUZaJjh49SseOHfHx8aFaioFy\nAgAAIABJREFUtWq88MILREVFMW3aNPr16+d0vGRuvfVWpyOIiORIZcuWZdy4cVpOyc3NmzePdu3a\ncfTo0WTbAgIC2LdvHxcuXHAgmWsqyjKJtZYuXbrQqVMnwsPD2bdvHxEREQwfPjxLjhcTE5Ml+xUR\nkZTFxcXRp0+fxPUyxb1dmyjW1SSygYGBeHh4EBYWlt2xUqSiLJN8//33FChQgCeeeAKIX1Nt3Lhx\nTJkyhcuXL3PkyBFCQkLw8fHh7bffBuDSpUs88MAD+Pn5UbduXebNmwfELwtxzz33EBAQwL333svx\n48cBCAkJYeDAgQQGBvLee+9RuXJl4uLiEvdVsWJFoqOjOXDgAG3btiUgIICmTZsm/sD9/vvvBAcH\nU69ePV577bXs/hKJiOR4c+bMYdKkSezfv9/pKJIKfn5+eHh4uLxM2aJFCy5evEijRo0cSOZarpmn\nLKmBAweyY8eOTN2nv78/H3/8cYrbf/nll8SK/JqiRYtSqVIlYmJi+Pnnn9mzZw+FChWiYcOGPPDA\nA/zxxx+UL1+er7/+GoDz588THR1N//79Wbx4MWXKlGHevHkMHz6cKVOmABAVFZW4NMS2bdtYu3Yt\nzZs3Z+nSpdx7773ky5ePZ555hokTJ+Lj48NPP/3Ec889x/fff88LL7xA37596dWrF6GhoZn69RER\nye0iIyMZPnw49evXp0ePHk7HkVQoWLAgtWrVclmUeXl5OZDon+XKoswdtW7dmlKlSgHQpUsX1q9f\nz/3338+gQYMYMmQI7dq1o2nTpuzZs4c9e/bQunVrAGJjYylXrlzifrp3737d83nz5tG8eXPmzp3L\nc889R0REBBs3bqRbt26J7a5evQrAhg0bWLBgAQCPPfYYQ4YMyfLzFhHJLUJDQ/njjz+YPHmyllPK\nQQICAnLMagu5sij7px6trFK7dm2+/PLL6967cOEChw8fxtPTM9lUEMYYatSowbZt21i2bBmvvfYa\nLVu2pHPnztSpU4cff/zR5XEKFy6c+LxDhw68+uqrnDlzhq1bt9KiRQsuXbpE8eLFU+wp1JQUIiJp\nd/bsWd577z3atm1Ly5YtnY4jaRAQEMD06dP5888/KV++vNNx/pFK/UzSsmVLLl++zIwZM4D4Hq5B\ngwbRu3dvChUqxHfffceZM2e4cuUKX331FU2aNOHPP/+kUKFCPProo7zyyits27aNmjVrcurUqcSi\nLDo6ml9++cXlMW+99VYaNmzICy+8QLt27fDw8KBo0aJ4e3vzxRdfAPE3IOzcuROAJk2aMHfuXABm\nzZqV1V8SEZFc4/333+fcuXNaTikHurYoubtNf+GKirJMYoxh0aJFfPHFF/j4+FCjRg0KFCjAyJEj\nAWjUqBFdu3bF19eXrl27EhgYyO7du2nUqBH+/v68/fbbvPbaa3h5efHll18yZMgQ/Pz88Pf3Z+PG\njSket3v37nz++efXXdacNWsWkydPxs/Pjzp16rB48WIAxo8fT2hoKPXq1ePYsWNZ+wUREckl/vjj\nDz755BN69eqV4uzw4r78/f255ZZbXN6B6W5yzdqXe/fupVatWg4lkrTQ90pEcpJevXoxf/58wsPD\nqVixotNxJB3q1KlDtWrVWLJkiSPHT+3al7lyTJmIiEhmOHfuHN9++y0DBw5UQZaDzZ49m9tvv93p\nGDelokxERCQFxYsXJzw8XDdJ5XB+fn5OR0gVjSkTERFx4dixY8TGxlKsWDGKFi3qdBzJgGs3abj7\nuDIVZSIiIjeIi4vjgQceoHPnzk5HkUzg4eHBsGHDWLZsmdNR/pEuX4qIiNwgNjaWXr16UaVKFaej\nSCYoUqQIYWFhVKtWzeko/0hFmYiIyA3y5cvHSy+95HQMyUQ1atRwOsJNZejypTGmpDHmO2NMeMKf\nJVJo19YY85sxZr8xZmiS9981xuwyxuw0xnxvjKmUkTxO8/DwwN/fP/Fx6NAhpyMBcOjQIWbPnu10\nDBGRHGHKlCnMmDGDnDhllKRs9+7dPPfcc5w6dcrpKCnK6JiyocAqa60PsCrh9XWMMR5AKHAfUBvo\nYYypnbD5A2utr7XWD/gKeDODeRxVsGBBduzYkfhIbbd3TExMluZSUSYikjpnzpxh0KBBzJ07V3dc\n5jKnT5/mP//5j1vP7J/RoqwjMD3h+XSgk4s2jYD91tqD1tooYG7C57DWXkjSrjBwOoN53E5kZCRP\nPPEE9erVo379+qxevRqAadOm0aFDB1q0aJG4jtoHH3xAw4YN8fX15c03/68+nTFjBr6+vvj5+fHY\nY48B8L///Y+goCDq169Pq1atOHnyJABr165N7KmrX78+Fy9eZOjQoaxbtw5/f3/GjRuXzV8BEZGc\nY+TIkZw/f17LKeVC9evXB9x7uaWMjikra609nvD8BFDWRZs7gCNJXh8Fgq69MMa8B/QCriR9/0bG\nmGeAZwAqVbr5Vc6QkJBk77Vr146XX345XdvXrFlz02NeuXIFf39/ALy9vVm0aBGhoaEYY9i9ezdh\nYWG0adOGffv2AbBt2zZ27dpFyZIlWbFiBeHh4fz8889Ya+nQoQM//PADpUqVYsSIEWzcuJHSpUtz\n5swZAO6++242bdqEMYZJkyYxZswYPvzwQ8aOHUtoaChNmjQhIiKCAgUKMGrUKMaOHcvSpUtveg4i\nInnVoUOHmDBhAr1796ZevXpOx5FMVqxYMapXr+7W02LctCgzxqwEXE2DOzzpC2utNcak+QK8tXY4\nMNwYMwwYB/ROod1nwGcQv8xSWo+THa5dvkxq/fr19O/fH4A777yTypUrJxZlrVu3pmTJkgCsWLGC\nFStWJFbyERERhIeHs3PnTrp160bp0qUBEtsfPXqU7t27c/z4caKiovD29gbiFx1/6aWX6NmzJ126\ndKFChQpZf+IiIrnA66+/zi233MI777zjdBTJIgEBAWzatMnpGCm6aVFmrW2V0jZjzEljTDlr7XFj\nTDngLxfNjgFJ16aokPDejWYB39wsT2rdrGcro9szQ+HChROfW2sZNmwYzz777HVtJkyY4PKz/fv3\n56WXXqJDhw6sWbOGt956C4ChQ4fywAMPsGzZMpo0acLy5cuzLL+ISG6xfft2Pv/8c4YOHar/zOZi\nDRo0YN68eZw+fZpSpUo5HSeZjI4pWwI8nvD8cWCxizabAR9jjLcxxgt4OOFzGGN8krTrCOxw8fkc\nrWnTpsyaNQuAffv2cfjwYWrWrJms3b333suUKVOIiIgA4meS/uuvv2jRogVffPEFp0/HD7e7dvny\n/Pnz3HHHHQBMnz49cT8HDhygXr16DBkyhIYNGxIWFkaRIkW4ePFilp6niEhOZa3llVdeoVSpUgwd\nmux+NclFAgICANz2EmZGi7JRQGtjTDjQKuE1xpjyxphlANbaGKAfsBzYC8y31v5y7fPGmD3GmJ1A\nC2BQBvO4neeee464uDjq1atH9+7dmTZtGvnz50/Wrk2bNjzyyCMEBwdTr149HnzwQS5evEidOnUY\nPnw499xzD35+fonz5rz11lt069aNgICAxEubAB9//DF169bF19eXfPnycd999+Hr64uHhwd+fn4a\n6C8icoMVK1awatUqXn/9dYoVK+Z0HMlCDRo0ANx3sL/JifOwBAYG2i1btlz33t69e6lVq5ZDiSQt\n9L0SEXfSvXt3tmzZwt69e/Hy8nI6jmSxqlWrEhAQwBdffJFtxzTGbLXWBt6snWb0FxGRPG3WrFkc\nPnxYBVkeERwcnDhUyN2oKBMRkTzN09OTqlWrOh1Dssnnn3/uthMDZ3RMmYiIiEiO4a4FGagoExER\nkTzk6tWrhISE8OmnnzodJRkVZSIiIpJn5M+fn+joaC5fvux0lGQ0pkxERETylA0bNjgdwSX1lGUi\nDw+PxMXA/f39OXToUKbu/6OPPqJ27dr4+vrSsmVL/vjjD5ft3nvvPerUqYOvry/+/v789NNPdO7c\nGX9/f6pXr06xYsUSM27cuJGQkBBq1qyJr68vd955J/369ePcuXOZml1ERET+mYqyTHRt7ctrjypV\nqqTqczExMalqV79+fbZs2cKuXbt48MEHGTx4cLI2P/74I0uXLk1c7HzlypVUrFiRRYsWsWPHDiZN\nmkTTpk0TM951111A/C3hu3btYteuXeTPn5+OHTum+rxvFBsbm67zExERyQ7bt2/H29s7W5ZUTAsV\nZVksMjKSJ554gnr16lG/fn1Wr14NwLRp0+jQoQMtWrSgZcuWAIwePZp69erh5+fncqmP5s2bU6hQ\nIQAaN27M0aNHk7U5fvw4pUuXTlw1oHTp0pQvXz7Veb28vBgzZgyHDx9m586dybavWLGC4OBgGjRo\nQLdu3RLneqlSpQpDhgyhQYMGfPHFF4SEhDBw4EACAwMZP358qo8vIiKS1e644w4OHTrkdsst5dox\nZSEhITdt065dO15++eXE9je+Tio11fSVK1fw9/cHwNvbm0WLFhEaGooxht27dxMWFkabNm3Yt28f\nQGJvVsmSJfnmm29YvHgxP/30E4UKFUpc4zIlkydP5r777kv2fps2bXjnnXeoUaMGrVq1onv37txz\nzz03zZ7UtSWZwsLC8PPzS3z/77//ZsSIEaxcuZLChQszevRoPvroI9544w0ASpUqlfgDPnHiRKKi\norhx5QURERGn3XbbbVSoUMHtllvKtUWZE65dvkxq/fr19O/fH4A777yTypUrJxZlrVu3pmTJkgCs\nXLmSJ554IrEn7Nr7rnz++eds2bKFtWvXJtt26623snXrVtatW8fq1avp3r07o0aNonfv3mk6F1fL\nb23atIlff/2VJk2aABAVFUVwcHDi9u7du1/X/sbXIiIi7iIgIEBFWXZJ63XiG9tnx3XmwoULp/kz\nK1eu5L333mPt2rUuFzaH+J6ukJAQQkJCqFevHtOnT09TURYbG8vu3buTrU9praV169bMmTPH5edu\nPJ/0nJ+IiEh2aNCgAUuWLOHixYsUKVLE6TiAxpRluaZNmzJr1iwA9u3bx+HDh6lZs2aydq1bt2bq\n1KmJ86a4uny5fft2nn32WZYsWcJtt93m8ni//fYb4eHhia937NhB5cqVU503OjqaYcOGUbFiRXx9\nfa/b1rhxYzZs2MD+/fsBuHTpUmKvn4iISE4SEBCAtZY9e/Y4HSVRru0pcxfPPfccffv2pV69enh6\nejJt2jSXPVxt27Zlx44dBAYG4uXlxf3338/IkSOva/PKK68QERFBt27dAKhUqRJLliy5rk1ERAT9\n+/fn3LlzeHp6Ur16dT777LOb5uzZsyf58+fn6tWrtGrVisWLFydrU6ZMGaZNm0aPHj24evUqACNG\njKBGjRqp/nqIiIi4gxYtWnDq1ClKly7tdJRExtXYIXcXGBhobxxAvnfv3mSX28Q96XslIiJ5iTFm\nq7U28GbtdPlSRERExA2oKBMRERFxA7mqKMuJl2LzGn2PREREXMs1RVmBAgU4ffq0/tF3Y9ZaTp8+\nTYECBZyOIiIi4nZyzd2XFSpU4OjRo5w6dcrpKPIPChQoQIUKFZyOISIi4nZyTVGWL18+vL29nY4h\nIiIiki655vKliIiISE6mokxERETEDagoExEREXEDOXJGf2PMKeCPLD5MaeDvLD6GO8vL55+Xzx3y\n9vnr3POuvHz+efncIXvOv7K1tszNGuXIoiw7GGO2pGZJhNwqL59/Xj53yNvnr3PPm+cOefv88/K5\ng3udvy5fioiIiLgBFWUiIiIibkBFWco+czqAw/Ly+eflc4e8ff4697wrL59/Xj53cKPz15gyERER\nETegnjIRERERN6CiTERERMQNqChzwRjT1hjzmzFmvzFmqNN5sosxpqIxZrUx5ldjzC/GmBeczpTd\njDEexpjtxpilTmfJbsaY4saYL40xYcaYvcaYYKczZRdjzLCEn/s9xpg5xpgCTmfKSsaYKcaYv4wx\ne5K8V9IY850xJjzhzxJOZsxKKZz/Bwk/+7uMMYuMMcWdzJhVXJ17km2DjDHWGFPaiWxZLaVzN8b0\nT/je/2KMGeNUPlBRlowxxgMIBe4DagM9jDG1nU2VbWKAQdba2kBj4Pk8dO7XvADsdTqEQ8YD31pr\n7wT8yCNfB2NMFeAZIMBaWxfwAB52MlM2mAa0veG9ocAqa60PsCrhdW41jeTn/x1Q11rrC+wDhmV3\nqGwyjeTnjjGmItAGOJzdgbLRNG44d2NMc6Aj4GetrQOMdSBXIhVlyTUC9ltrD1pro4C5xH/Dcj1r\n7XFr7baE5xeJ/0f5DmdTZR9jTAXgAWCS01mymzGmGNAMmAxgrY2y1p5zNlW2uQBEAwWNMZ5AIeBP\nZyNlLWvtD8CZG97uCExPeD4d6JStobKRq/O31q6w1sYkvNwEVMj2YNkghe89wDhgMJBr7/5L4dz7\nAqOstVcT2vyV7cGSUFGW3B3AkSSvj5KHCpNrEnoP6gM/OZskW31M/C+lOKeDOMAbOAVMTbh8O8kY\nU9jpUNnBWnuG+P8dHwaOA+ettSucTeWIstba4wnPTwBlnQzjsCeBb5wOkV2MMR2BY9banU5ncUAN\noKkx5idjzFpjTEMnw6gok2SMMbcCC4CB1toLTufJDsaYdsBf1tqtTmdxiCfQAPiPtbY+cIncffkq\nkTGmGvAi8YVpeaCwMeZRZ1M5y8bPlZRre0z+iTFmOPFDOWY5nSU7GGMKAa8CbzidxSGeQEnih+y8\nAsw3xhinwqgoS+4YUDHJ6woJ7+UJxph8xBdks6y1C53Ok42aAB2MMYeIv2TdwhjzubORstVR4Ki1\n9lrP6JfEF2l5QSCw0Vp7ylobDSwE7nI4kxNOGmPKAST86ehlHCcYY3oD7YCeNu9M4lmN+P+Q7Ez4\n/VcB2GaMud3RVNnnKLDQxvuZ+Csljt3ooKIsuc2AjzHG2xjjRfyA3yUOZ8oWCf87mAzstdZ+5HSe\n7GStHWatrWCtrUL89/x7a22e6S2x1p4Ajhhjaia81RL41cFI2ek3oLExplDC34GW5JGbHG6wBHg8\n4fnjwGIHs2Q7Y0xb4ocvdLDWXnY6T3ax1u621t5mra2S8PvvKNAg4XdCXvAV0BzAGFMD8AL+diqM\nirIbJAz07AcsJ/4X83xr7S/Opso2TYDHiO8l2pHwuN/pUJJt+gOzjDG7AH9gpMN5soW1dgcwA9gC\n7Cb+96LbLLuSFYwxc4AfgZrGmKPGmKeAUUBrY0w40Crhda6Uwvn/GygCfJfwu2+ioyGzSArnniek\ncO5TgKoJ02TMBR53spdUyyyJiIiIuAH1lImIiIi4ARVlIiIiIm5ARZmIiIiIG1BRJiIiIuIGVJSJ\niIiIuAEVZSIiIiJuQEWZiIiIiBtQUSYiIiLiBlSUiYiIiLgBFWUiIiIibkBFmYiIiIgbUFEmIiIi\n4gZUlImIiIi4ARVlIiIiIm5ARZmIiIiIG1BRJiIiIuIGVJSJiIiIuAEVZSIiIiJuQEWZiIiIiBtQ\nUSYieZox5pAx5ooxJsIYc9IY87kxppjTuUQk71FRJiIC7a21twJ+QD3gNYfziEgepKJMRCSBtfYE\nsByo43QWEcl7VJSJiCQwxlQA7gN+djqLiOQ9xlrrdAYREccYYw4BpQEL3AosAbpaa2OczCUieY96\nykREoJO1tggQAjQHApyNIyJ5kYoyEZEE1tq1wARgtNNZRCTvUVEmInK9j4FGxpjGTgcRkbxFRZmI\nSBLW2lPAdGCo01lEJG/RQH8RERERN6CeMhERERE3oKJMRERExA2oKBMRERFxAyrKRERERNyAp9MB\n0qN06dK2SpUqTscQERERuamtW7f+ba0tc7N2ObIoq1KlClu2bHE6hoiIiMhNGWP+SE07Xb4UERER\ncQMqykRERETcgIoyERERETegokxERETEDagoExEREXEDKspERERE3ICKMhERERE3oKJMRERExA2o\nKBMREZE8Z+XKlaxYscLpGNdRUSYiIiJ5xqlTp+jVqxetW7dm1KhRTse5jooyERERyTM+/PBD5s6d\ny2uvvcayZcucjnMdY611OkOaBQYGWq19KSIiIqkRHh5OREQE9evX5+LFixw+fJg6depk2/GNMVut\ntYE3a5cjFyQXERERSY2rV6/SrFkzvL292bhxI0WKFMnWgiwtdPlSREREcp0dO3YQFxdH/vz5mTFj\nBgsWLHA60k2pKBMREZFc49y5c/Tt25f69eszc+ZMAFq3bk25cuUcTnZzunwpIiIiOZ61lgULFjBg\nwABOnjzJwIED6dq1q9Ox0kRFmYiIiORoR44c4fnnn+d///sf/v7+LFmyhMDAm46rdzu6fCkiIiI5\nUmxsLOPHj6dWrVqsWrWKsWPHsnnz5hxZkIF6ykRERCQHOnHiBO3bt2fLli3cd999fPrpp1SpUsXp\nWBmiokxERERyDGstxhjKlClD2bJlmTt3Lg899BDGGKejZZguX4qIiEiOsHr1aho3bszp06fx8PBg\n6dKldO/ePVcUZKCiTERERHKIqKgoLly4wPHjx52OkiV0+VJERETckrWWKVOmcOrUKYYOHcq9997L\n7t278fTMneWLespERETE7YSFhRESEsLTTz/NypUriYuLA8i1BRmoKBMRERE3cvXqVd5++238/PzY\nvXs3kyZNYsWKFdxyS+4vWXJvuSkiIiI5yrp163jmmWcICwujR48ejBs3jrJlyzodK9vk/rJTRERE\n3NrZs2fp06cPzZo1IzIykm+++YbZs2fnqYIMVJSJiIiIw1555RWmTp3Kyy+/zJ49e2jbtq3TkRxh\nrLVOZ0izwMBAu2XLFqdjiIiISDpEREQwd+5c6tSpQ3BwMH/++ScnT56kfv36TkfLEsaYrdbam679\npJ4yERERyXJnzpxh7969QPxg/r59+zJ//nwAypcvn2sLsrTQQH8RERHJEmfPnmXx4sXMnz+f7777\njiZNmrBmzRpKlSrF3r17qVatmtMR3YqKMhEREck0586dY8mSJcyfP58VK1YQHR1NlSpVeOmll3jo\noYcS21WvXt3BlO5JRZmIiIhk2Pfff8+4ceNYvnw50dHRVKpUiRdeeIGHHnqIwMDAXLM+ZVZSUSYi\nIiJpdvHiRZYsWUKLFi0oV64cR44cYefOnQwYMIBu3brRqFEjFWJplCkD/Y0xbY0xvxlj9htjhrrY\nbowxnyRs32WMaZBkW3FjzJfGmDBjzF5jTHBmZBIREZHMdfHiRU6dOgXA1q1befTRR/nqq68A6Nmz\nJ4cOHWLs2LEEBQWpIEuHDBdlxhgPIBS4D6gN9DDG1L6h2X2AT8LjGeA/SbaNB7611t4J+AF7M5pJ\nREREMse16Su6dOnCbbfdxqhRowBo2rQpGzZs4NlnnwXi16TMC0shZaXMuHzZCNhvrT0IYIyZC3QE\nfk3SpiMww8ZPirYpoXesHHAZaAb0BrDWRgFRmZBJRERE0unSpUt8/fXXzJ8/n6+//prIyEjKlStH\nnz59ePjhhwHw8PDgrrvucjhp7pIZRdkdwJEkr48CQalocwcQA5wCphpj/ICtwAvW2ks3HsQY8wzx\nvWxUqlQpE2KLiIhIUt988w1Tp05l6dKlXLlyhdtvv52nn36ahx56iCZNmqgnLIs5/dX1BBoA/7HW\n1gcuAcnGpAFYaz+z1gZaawPLlCmTnRlFRERypcjISBYvXkxcXBwQX5StXbuWJ554gjVr1nD06FEm\nTJhA06ZNVZBlg8z4Ch8DKiZ5XSHhvdS0OQoctdb+lPD+l8QXaSIiIpLFPvnkEzp16sSGDRsAGDFi\nBH/++SehoaHcc889eHh4OJwwb8mMy5ebAR9jjDfxhdbDwCM3tFkC9EsYbxYEnLfWHgcwxhwxxtS0\n1v4GtOT6sWh5wtmzZzl48OB1j0qVKtG/f3+KFi3qdDwREckFrLWsXLmS0NBQHnnkER566CGeeOIJ\n/P39CQ6On/hA/+Y4K8NFmbU2xhjTD1gOeABTrLW/GGP+lbB9IrAMuB/YT/zg/ieS7KI/MMsY4wUc\nvGFbrhAdHc2RI0euK7oOHDiQ+PzcuXPXtS9VqhSnT5/m448/Zvjw4fTt25f8+fM7lF5ERHKyCxcu\nMH36dEJDQ/ntt98oU6YM7du3B6BMmTK0adPG4YRyjYm/ITJnCQwMtFu2bHE6xnVu7O1KWnQdPnyY\n2NjYxLb58uXD29ubqlWrunwUKVKEzZs38+qrr7Jy5UoqVarEO++8w6OPPqquZBERSZVff/2V0NBQ\nZsyYQUREBEFBQfTr149u3brpP/rZzBiz1VobeNN2KspSJ2lvV9KCK6XerjJlyiQWWdWqVbuu6Cpf\nvnyqi6uVK1cydOhQtm7dSu3atRk5ciQdOnTQpHwiIuLS999/z4gRI1i9ejX58+fn4Ycf5vnnn6dh\nw4ZOR8uzUluUaZklF3bs2MG33357XdF1Y2+Xl5dXYm9XcHDwdUWXt7c3RYoUyZQsrVq1YvPmzSxY\nsIDhw4fTqVMngoODGTVqFM2aNcuUY4iISM72119/UahQIW699VZ+//13Dhw4wPvvv89TTz2FZizI\nOdRT5sK///1v+vfvz2233eby8mK1atUoX758tt8eHBMTw9SpU3nrrbf4888/ue+++3j//ffx8/PL\n1hwiIuI+tm7dyl133cXYsWPp378/0dHR3HLLLRru4kZ0+TIDIiIiALj11luz7BgZcfnyZf7973/z\n/vvvc/78eXr06MG7775L1apVnY4mIiJZLDIyknnz5hEdHc3TTz9NbGwsb7zxBo8//jg1atRwOp64\noKIsDzh79ixjxoxh/PjxREdH8+yzz/L6669TtmxZp6OJiEgm++OPP/jPf/7DpEmTOH36NCEhIaxe\nvdrpWJIKqS3KND1vDlaiRAnef/999u/fz9NPP83EiROpVq0ar7/+OufPn3c6noiIZJC1lu+++45O\nnTpRtWpVPvjgA5o1a8aqVav4/vvvnY4nmUxFWS5Qvnx5/vOf/7B3717atWvHiBEjqFZ1yKx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F577TXatGlDWlqa07FE5CqolImIFDG+vr5MmjSJmTNnsm7dOsLCwti4caPTsUTkd6iUiYgUUb17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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4c9d6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vecm_res.plot_forecast(steps=5, n_last_obs=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative representations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAR-representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every VECM has a corresponding VAR-model. To get its parameter matrices $A_1, \\ldots, A_p$ (where $p$ is the number of lags in levels) we can use the `var_rep()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.03433973  0.22617928]\n",
      " [ 0.19486172  1.17273842]]\n"
     ]
    },
    {
     "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(vecm_res.var_rep[0])  # A_1\n",
    "vecm_res.var_rep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MA-representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the first $i+1$ coefficient matrices of the MA-representation, we can call the `ma_rep()` method passing $i$ as the `maxn` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.        ,  0.        ],\n",
       "        [ 0.        ,  1.        ]],\n",
       "\n",
       "       [[ 0.03433973,  0.22617928],\n",
       "        [ 0.19486172,  1.17273842]],\n",
       "\n",
       "       [[-0.0082822 ,  0.20229393],\n",
       "        [ 0.21955708,  1.13063646]]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.ma_rep(maxn=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structural analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Granger causality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `test_granger_causality()` method allows testing for Granger-causality. The returned `CausalityTestResults` object offers a summary of the test via its `summary()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Granger causality F-test. H_0: R does not Granger-cause Dp. Conclusion: reject H_0 at 5% significance level.</caption>\n",
       "<tr>\n",
       "  <th>Test statistic</th> <th>Critical value</th> <th>p-value</th>    <th>df</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>3.531</td>          <td>2.423</td>      <td>0.008</td>  <td>(4, 176)</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "granger_results = vecm_res.test_granger_causality(caused=\"Dp\", signif=0.05)\n",
    "granger_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we print a `CausalityTestResults` object we also get all the relevant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.tsa.vector_ar.hypothesis_test_results.CausalityTestResults object. H_0: R does not Granger-cause Dp: reject at 5% significance level. Test statistic: 3.531, critical value: 2.423>, p-value: 0.008>\n"
     ]
    }
   ],
   "source": [
    "print(granger_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also directly access values of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 0.008449053735648273)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "granger_results.signif, granger_results.pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantaneous causality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tests for instantaneous causality are executed in a similar fashion using the `test_inst_causality()` method. Again, a `CausalityTestResults` object is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Instantaneous causality Wald-test. H_0: R does not instantaneously cause Dp. Conclusion: fail to reject H_0 at 5% significance level.</caption>\n",
       "<tr>\n",
       "  <th>Test statistic</th> <th>Critical value</th> <th>p-value</th> <th>df</th>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>0.6068</td>          <td>3.841</td>      <td>0.436</td>   <td>1</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst_caus_dp_r = vecm_res.test_inst_causality(causing=\"Dp\")\n",
    "inst_caus_r_dp = vecm_res.test_inst_causality(causing=\"R\")\n",
    "inst_caus_r_dp.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compare `HypothesisTestResults` objects (`HypothesisTestResults` is the base class of `CausalityTestResults`). Since instantaneous causality is a symmetric relation (this means that the roles of the caused and the causing variables may be swapped without effect), the following two tests are recognized as equal. Granger causality is not a symmetric relation, so swapping variables leads to `CausalityTestResults` instances which are not equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst_caus_dp_r == inst_caus_r_dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "granger_results == vecm_res.test_granger_causality(caused=\"R\", signif=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impulse-Response-Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyze how an impulse in one variable affects the system, we can use the `irf()` method and call the returned object's `plot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VQ/TMmbDppnDNNbnLwl575c/jLW/Jw4w98gjcdBN8+MO5C8Pxx8M228BBB+WRHO69t77j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\nGGAlSZJUKgZYSZIklYoBVpIkSaXy/wORVdxqjWUBTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4cd37470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_periods = 30\n",
    "ir = vecm_res.irf(periods=num_periods)\n",
    "ir.plot(plot_stderr=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing for nonnormality and for residual autocorrelation is possible with the `test_normality()` and `test_whiteness()` methods which return a `NormalityTestResults` and a `WhitenessTestResults` object respectively. Both mentioned classes are subclasses of `HypothesisTestResults` - just as in the case of the causality tests. So we again have the same possibilities as above for inspecting those objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing for Nonnormality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>normality (skew and kurtosis) test. H_0: data generated by normally-distributed process. Conclusion: fail to reject H_0 at 5% significance level.</caption>\n",
       "<tr>\n",
       "  <th>Test statistic</th> <th>Critical value</th> <th>p-value</th> <th>df</th>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>2.118</td>          <td>9.488</td>      <td>0.714</td>   <td>4</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_test = vecm_res.test_normality()\n",
    "norm_test.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.tsa.vector_ar.hypothesis_test_results.NormalityTestResults object. H_0: data generated by normally-distributed process: fail to reject at 5% significance level. Test statistic: 2.118, critical value: 9.488>, p-value: 0.714>\n"
     ]
    }
   ],
   "source": [
    "print(norm_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.11780091933902, 9.487729036781154, 0.714102780437923)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_test.test_statistic, norm_test.crit_value, norm_test.pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for residual autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Adjusted Portmanteau-test for residual autocorrelation. H_0: residual autocorrelation up to lag 12 is zero. Conclusion: fail to reject H_0 at 5% significance level.</caption>\n",
       "<tr>\n",
       "  <th>Test statistic</th> <th>Critical value</th> <th>p-value</th> <th>df</th>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>33.52</td>          <td>48.60</td>      <td>0.491</td>  <td>34</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_test = vecm_res.test_whiteness(nlags=12, adjusted=True)\n",
    "white_test.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.tsa.vector_ar.hypothesis_test_results.WhitenessTestResults object. H_0: residual autocorrelation up to lag 12 is zero: fail to reject at 5% significance level. Test statistic: 33.518, critical value: 48.602>, p-value: 0.491>\n"
     ]
    }
   ],
   "source": [
    "print(white_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33.51814228583752, 48.602367367294178, 0.49108854964101634)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_test.test_statistic, white_test.crit_value, white_test.pvalue"
   ]
  }
 ],
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