{
 "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.tsa.vector_ar.vecm import *\n",
    "\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f5966bd00>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\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>    -20.74</td>  <td>    -20.37*</td> <td> 9.838e-10</td>  <td>    -20.59*</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th>  <td>    -20.73</td>  <td>    -20.25</td>  <td> 9.984e-10</td>  <td>    -20.53</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>  <td>    -20.65</td>  <td>    -20.06</td>  <td> 1.079e-09</td>  <td>    -20.41</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>  <td>    -20.77*</td> <td>    -20.08</td>  <td> 9.561e-10*</td> <td>    -20.49</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>  <td>    -20.71</td>  <td>    -19.91</td>  <td> 1.020e-09</td>  <td>    -20.38</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>  <td>    -20.68</td>  <td>    -19.77</td>  <td> 1.054e-09</td>  <td>    -20.31</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th>  <td>    -20.64</td>  <td>    -19.63</td>  <td> 1.094e-09</td>  <td>    -20.23</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>7</th>  <td>    -20.59</td>  <td>    -19.47</td>  <td> 1.162e-09</td>  <td>    -20.13</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>8</th>  <td>    -20.52</td>  <td>    -19.29</td>  <td> 1.247e-09</td>  <td>    -20.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>9</th>  <td>    -20.51</td>  <td>    -19.17</td>  <td> 1.271e-09</td>  <td>    -19.97</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>10</th> <td>    -20.50</td>  <td>    -19.05</td>  <td> 1.295e-09</td>  <td>    -19.91</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccc}\n",
       "\\toprule\n",
       "            & \\textbf{AIC} & \\textbf{BIC} & \\textbf{FPE} & \\textbf{HQIC}  \\\\\n",
       "\\midrule\n",
       "\\textbf{0}  &      -20.74  &     -20.37*  &   9.838e-10  &      -20.59*   \\\\\n",
       "\\textbf{1}  &      -20.73  &      -20.25  &   9.984e-10  &       -20.53   \\\\\n",
       "\\textbf{2}  &      -20.65  &      -20.06  &   1.079e-09  &       -20.41   \\\\\n",
       "\\textbf{3}  &     -20.77*  &      -20.08  &  9.561e-10*  &       -20.49   \\\\\n",
       "\\textbf{4}  &      -20.71  &      -19.91  &   1.020e-09  &       -20.38   \\\\\n",
       "\\textbf{5}  &      -20.68  &      -19.77  &   1.054e-09  &       -20.31   \\\\\n",
       "\\textbf{6}  &      -20.64  &      -19.63  &   1.094e-09  &       -20.23   \\\\\n",
       "\\textbf{7}  &      -20.59  &      -19.47  &   1.162e-09  &       -20.13   \\\\\n",
       "\\textbf{8}  &      -20.52  &      -19.29  &   1.247e-09  &       -20.02   \\\\\n",
       "\\textbf{9}  &      -20.51  &      -19.17  &   1.271e-09  &       -19.97   \\\\\n",
       "\\textbf{10} &      -20.50  &      -19.05  &   1.295e-09  &       -19.91   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{VECM Order Selection (* highlights the minimums)}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 0, 3, 0)"
      ]
     },
     "execution_count": 40,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_test = select_coint_rank(data, 0, 3, method=\"trace\", signif=0.05)\n",
    "rank_test.rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "recall: rank of a matrix is defined as the number of linearly independent rows it contains\n",
    "- if rank = 0 then yt are I(1) but not cointegrated.\n",
    "- if 0 < rank < n then yt are cointegrated with r = rank linearly independent long-run relationships"
   ]
  },
  {
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Johansen cointegration test using trace test statistic with 5% 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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{cccc}\n",
       "\\toprule\n",
       "\\textbf{r\\_0} & \\textbf{r\\_1} & \\textbf{test statistic} & \\textbf{critical value}  \\\\\n",
       "\\midrule\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{rrrr}\n",
       "            0 &             2 &                   17.17 &                   15.49  \\\\\n",
       "            1 &             2 &                   3.032 &                   3.841  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Johansen cointegration test using trace test statistic with 5% significance level}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 45,
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Johansen cointegration test using trace test statistic with 5% 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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\valte\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency Q-DEC will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "model = VECM(data, deterministic=\"ci\", seasons=4,\n",
    "             k_ar_diff=lag_order.aic,  # =3\n",
    "             coint_rank=rank_test.rank)  # =1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "vecm_res = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccccc}\n",
       "\\toprule\n",
       "                 & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{season1} &       0.0162  &        0.005     &     3.554  &         0.000        &        0.007    &        0.025     \\\\\n",
       "\\textbf{season2} &       0.0177  &        0.005     &     3.690  &         0.000        &        0.008    &        0.027     \\\\\n",
       "\\textbf{season3} &       0.0341  &        0.005     &     7.464  &         0.000        &        0.025    &        0.043     \\\\\n",
       "\\textbf{L1.Dp}   &      -0.3339  &        0.141     &    -2.364  &         0.018        &       -0.611    &       -0.057     \\\\\n",
       "\\textbf{L1.R}    &       0.0677  &        0.095     &     0.715  &         0.474        &       -0.118    &        0.253     \\\\\n",
       "\\textbf{L2.Dp}   &      -0.3874  &        0.114     &    -3.399  &         0.001        &       -0.611    &       -0.164     \\\\\n",
       "\\textbf{L2.R}    &      -0.0030  &        0.095     &    -0.032  &         0.975        &       -0.190    &        0.184     \\\\\n",
       "\\textbf{L3.Dp}   &      -0.3457  &        0.076     &    -4.524  &         0.000        &       -0.495    &       -0.196     \\\\\n",
       "\\textbf{L3.R}    &       0.0204  &        0.092     &     0.222  &         0.824        &       -0.160    &        0.201     \\\\\n",
       "                 & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{season1} &       0.0074  &        0.005     &     1.518  &         0.129        &       -0.002    &        0.017     \\\\\n",
       "\\textbf{season2} &      -0.0019  &        0.005     &    -0.381  &         0.703        &       -0.012    &        0.008     \\\\\n",
       "\\textbf{season3} &      -0.0015  &        0.005     &    -0.318  &         0.750        &       -0.011    &        0.008     \\\\\n",
       "\\textbf{L1.Dp}   &      -0.2024  &        0.150     &    -1.350  &         0.177        &       -0.496    &        0.092     \\\\\n",
       "\\textbf{L1.R}    &       0.2724  &        0.101     &     2.709  &         0.007        &        0.075    &        0.469     \\\\\n",
       "\\textbf{L2.Dp}   &      -0.2180  &        0.121     &    -1.802  &         0.072        &       -0.455    &        0.019     \\\\\n",
       "\\textbf{L2.R}    &      -0.0164  &        0.101     &    -0.162  &         0.872        &       -0.215    &        0.182     \\\\\n",
       "\\textbf{L3.Dp}   &      -0.1056  &        0.081     &    -1.301  &         0.193        &       -0.265    &        0.053     \\\\\n",
       "\\textbf{L3.R}    &       0.2253  &        0.098     &     2.303  &         0.021        &        0.034    &        0.417     \\\\\n",
       "             & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ec1} &      -0.6317  &        0.167     &    -3.791  &         0.000        &       -0.958    &       -0.305     \\\\\n",
       "             & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ec1} &       0.3972  &        0.177     &     2.245  &         0.025        &        0.050    &        0.744     \\\\\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{beta.1} &       1.0000  &            0     &         0  &         0.000        &        1.000    &        1.000     \\\\\n",
       "\\textbf{beta.2} &      -0.2508  &        0.044     &    -5.671  &         0.000        &       -0.338    &       -0.164     \\\\\n",
       "\\textbf{const}  &       0.0107  &        0.003     &     3.142  &         0.002        &        0.004    &        0.017     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Det. terms outside the coint. relation & lagged endog. parameters for equation Dp}\n",
       "\\end{center}"
      ],
      "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": 58,
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.63174988],\n",
       "       [ 0.39724572]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16663211],\n",
       "       [0.17693968]])"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {},
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecm_res.predict(steps=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "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, values in zip((\"forecast\", \"lower\", \"upper\"), vecm_res.predict(steps=5, alpha=0.05)):\n",
    "    print(text+\":\", values, 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": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vecm_res.plot_forecast(steps=5, plot_conf_int=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "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": 67,
   "metadata": {},
   "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": 67,
     "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": 68,
   "metadata": {},
   "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": 68,
     "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": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{cccc}\n",
       "\\toprule\n",
       "\\textbf{Test statistic} & \\textbf{Critical value} & \\textbf{p-value} & \\textbf{df}  \\\\\n",
       "\\midrule\n",
       "         3.531          &          2.423          &      0.008       &   (4, 176)   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Granger causality F-test. H_0: R does not Granger-cause Dp. Conclusion: reject H_0 at 5% significance level.}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 69,
     "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": 70,
   "metadata": {},
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 0.008449053735648273)"
      ]
     },
     "execution_count": 71,
     "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": 72,
   "metadata": {},
   "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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{cccc}\n",
       "\\toprule\n",
       "\\textbf{Test statistic} & \\textbf{Critical value} & \\textbf{p-value} & \\textbf{df}  \\\\\n",
       "\\midrule\n",
       "         0.6068         &          3.841          &      0.436       &      1       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Instantaneous causality Wald-test. H_0: R does not instantaneously cause Dp. Conclusion: fail to reject H_0 at 5% significance level.}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 72,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst_caus_dp_r == inst_caus_r_dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ADxk4cKDWr19vt9spam7WrFmaPXu2PvnkE7vT+wAA4A+45xYAAAAA4PcItwAAAAAAv0e4BQAAAAD4Pe65BQAAAAD4PSq3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO8RbgEAAAAAfo9wCwAAAADwe4RbAAAAAIDfI9wCAAAAAPwe4RYAAAAA4PcItwAAAAAAv0e4BQAAAAD4PcItAAAAAMDvEW4BAAAAAH6PcAsAAAAA8HuEWwAAAACA3yPcAgAAAAD8HuEWcIOlS5fKYrEUL6GhoWrZsqUGDRqkuXPnKiMjw9tNLOZPbQUAwN3KXxeDgoLUqlUrXXvttdq1a5fHz891Gai+IG83AKjNXn75ZXXt2lUnT55URkaGNm3apEceeUSPP/64kpKSdMkll3i7icX8qa0AALib7bp44sQJbd68WQ8//LA++eQT/fTTT2rSpInHzs91Gag+wi3gRt27d1dsbGzx81GjRmnq1Km64IILdNVVV2nXrl2KjIz0YgtL+FNbAQBwt9LXxYEDB6qwsFAPPvig3nzzTY0fP96j55e4LgPVQbdkwMPatWunJ554Qjk5OVqyZIkkadasWbJYLNqxY4euuuoqhYWFKTw8XDfccIMOHTrk9LmefPJJvfnmmy5tqzvbCwCAr7IFzYMHDzr1+ppekyXP/g0B+CPCLeAFl112mQIDA7Vhw4Yy20eOHKkzzjhDq1ev1qxZs/Tmm29qyJAhOnnypFPn2bZtm0aPHl2ji2llbXVHewEA8FV79uyRJHXp0sWp17vimix57m8IwB/RLRnwgoYNGyoiIkIHDhwos/2qq67So48+KkmKj49XZGSkrr/+eq1atUrXX3+9w+dZtmyZJGn06NFatWqVRowY4bK2uqO9AAD4isLCQhUUFBTfc/vQQw9pwIABuvLKK506niuuyZLn/oYA/BGVW8BLrFZrhW3lLz6jR49WUFCQPvnkkwr7ZmZmlhlN0d4SFBSkFStW6OTJkxo9erTTXanstdXR9gIA4E/69OmjevXqqXHjxrr00kvVpEkTvfXWWwoKqlgb8uQ1War53xBAbUXlFvCCo0eP6vDhwzr77LPLbG/ZsmWZ50FBQWrWrJkOHz5c4RiNGzfW888/f8pzvf/++1qzZo2GDx+uZs2auaytjrYXAAB/smzZMkVHRysnJ0dJSUlasmSJrrvuOq1bt67Cvp66Jkuu+RsCqK0It4AXvPfeeyosLNTAgQPLbE9PT1ebNm2KnxcUFOjw4cN2L4AhISGaOHHiKc/z7rvv6uqrr9bKlSvtftvsbFsdbS8AAP4kOjq6eBCpQYMGqbCwUC+88IJWr16tq6++usy+nrom245T078hgNqKbsmAh6Wmpuqee+5ReHi4br311jI/W758eZnnq1atUkFBgd1gWR2PPfaYhg0b5vRFtKq2uqO9AAD4qkcffVRNmjTRzJkzVVRU5PDra3pNljz7NwTgj6jcAm703XffqaCgQAUFBcrIyNDGjRv18ssvKzAwUG+88YaaN29eZv+1a9cqKChIgwcP1vfff68HHnhAPXr00OjRo506/zvvvKP69etX6yLqaFvd0V4AAHxVkyZNlJiYqOnTp2vFihW64YYbHHq9I9dkyft/QwD+iHALuJFtkvfg4GCddtppio6O1t///ndNnDix0rA4a9YsLV68WBaLRcOGDdP8+fMVHBzs1PkbN27stra6o70AAPiyO+64Q08//bTmzJmj6667ToGBgdV+rSPXZMn7f0MA/shirWwYVAAeM2vWLM2ePVuHDh1SRESEt5tzSv7WXgAAaiuuyUAJ7rkFAAAAAPg9wi0AAAAAwO/RLRkAAAAA4Peo3AIAAAAA/B7hFgAAAADg9/xiKqCioiIdOHBAjRs3lsVi8XZzAAC1iNVqVU5Ojlq3bq2AAL7zdRbXagCAu1T3Wu0X4fbAgQOKiorydjMAALXYvn371LZtW283w29xrQYAuNuprtV+EW5tk17v27dPYWFhXm4NAKA2yc7OVlRUVPG1Bs7hWg0AcJfqXqv9ItzaujeFhYVxwQQAuAVdaWuGazUAwN1Oda3m5iIAAAAAgN8j3AIAAAAA/B7hFgAAAADg9wi3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO8RbgEAAAAAfo9wCwAAAADwe4RbAAAAAIDfI9wCAAAAAPwe4RYAAAAA4PcItwAAAAAAv+dwuN2wYYOGDRum1q1by2Kx6M033zzla9avX6+YmBiFhobq9NNP17PPPutMWwEAAAAAsMvhcHv06FH16NFDTz/9dLX237Nnjy677DL1799fO3bs0D/+8Q/deeedWrNmjcONBQAAAADAniBHXzB06FANHTq02vs/++yzateunebPny9Jio6O1rZt2/T4449r1KhRdl+Tl5envLy84ufZ2dmONtOuRYukDz6Qxo6Vrr7aJYcEAACosc8/l1aulB54QGrWzNutcd6BA1KDBtJpp3m7JQDqIrffc7t161bFx8eX2TZkyBBt27ZNJ0+etPuauXPnKjw8vHiJiopySVu+/lp6+23pxx9dcjgAAACXuP9+acEC6Y47vN0S5/36q9S1q9SunbR2rbdbA6Aucnu4TU9PV2RkZJltkZGRKigoUGZmpt3XJCYmKisrq3jZt2+fS9pSv755PH7cJYcDAABwiZ9+Mo8rV0opKd5ti7NmzJBycswyapR0991SJXUMAHALj4yWbLFYyjy3Wq12t9uEhIQoLCyszOIKhFsAAOBrjh2Tfv+95PnkydKJE95rjzO++EJKSpIsFunGG822efOkQYOk/fu92zYAdYfbw23Lli2Vnp5eZltGRoaCgoLUzMM3ldjC7bFjHj0tAABApX75xTyGhUmtWpnn//63d9vkCKtVmj7drI8bJy1dKr3xhnk/mzdLPXtKH3/s1SYCqCPcHm7j4uKUUq5/zYcffqjY2FjVq1fP3acvg8otAADwNbt2mcfoaHPfrSTNnSv973/ea5MjkpOl9eulkBBpzhyzbcQI6auvpB49pEOHpPh46eGHpaIirzYVQC3ncLjNzc3Vzp07tXPnTklmqp+dO3cqNTVVkrlfdty4ccX7T5o0Sb/99pumTZumH3/8US+99JJefPFF3XPPPa55Bw4g3AIAAF9jC7GdO5vZHC69VMrPN92T/7qTy2cVFkp//7tZv+suM5iUTadO0tat0oQJJtTef790xRXS4cPeaSuA2s/hcLtt2zb17NlTPXv2lCRNmzZNPXv21MyZMyVJaWlpxUFXkjp27Kjk5GR9+umnOvfcc/XPf/5TCxcurHQaIHdq0MA8Em4BAICvsFVuO3c296w+/bQUGmq68q5c6d22ncorr0jffy81bSolJlb8ef360gsvSC+9ZN7TunVSr17Sl196vq0Aaj+H57kdOHBg8YBQ9ixdurTCtgsvvFBfffWVo6dyOSq3AADA19gqt126mMdOnUyV8/77palTpaFDpSZNvNe+yhw7ZubllcxIyVXNbTt+vAm1V19t7im+4ALpySel224zgR4AXMEjoyX7CsItAADwNaUrtzb33GPmjM3IMMHRF82fLx04IHXoIN1++6n379FD2rZNuuoq0+369tulG26QcnPd3VIAdUWdDLeMlgwAAHxBVpYJsFLZcBsSIi1ebNaffVb6/HPPt60qhw6VjOj88MOmvdURHi6tXi098YQUGCitWCGdf76ZSshX/Pe/0p13mu7Utt8NAP9QJ8MtlVsAAOALbFXbyEgzdU5pAweaqXWsVmnSJKmgwOPNq9RDD0k5Oaar8bXXOvZai0WaNk369FMz9dGPP0p9+kj/938m7HvL11+bwbwuvlh66inp5ptN+wYONKNYlxpSBoCPItwCAAB4iS3c2u63Le+xx8z9tjt3moGmfMGvv5ZUlR99VApw8q/JCy4wgXLsWBPgn3nGdMVOSvLsKNG//Wa+ROjZU/rgAykoyHSXjo01ozyvXy9NmSK1by+dd56Zpunnnz3XPgDVV6fCLaMlAwAAX1J6GiB7WrSQHnnErD/wgPT7755pV1VmzJBOniypctZE8+bSsmWmK3CXLlJ6uqkEDx1qQrQ7HT4s3X23Oe+rr5pAnZAg/fSTef7ll9Levebe4gEDTMV52zbpH/8wIfyss8zvZMcO35+yCagr6lS4pXILAAB8ib3BpMqbMEGKizMDL02Z4pFmVeqLL0xl1WIpCd2uMGiQ9M030uzZ5v7dDz6Qunc39/Pm5bnuPJL5O/Df/zajUs+bZwa3uugiE2Zfe81st2nf3szfu369lJYmPfecCfX16kk//GC6Z/fqJXXsaPb78EPXtxdA9VmsVc3r4yOys7MVHh6urKwshZW/IcUBaWlS69am+0xBAUPPAwBcd42p6/gcnXP++SZUrVljRhGuzDffmBBVWCi9+650+eWea6ON1WpC6Pr10o03SnZmf3SJXbukyZOljz4yz7t2NYNqXXhhzY5bUGDm5X3wQWn/frOtRw8T0uPjHfu78MgR6b33pLVrzdy9pQsnDRuaivbll5sKdFRUzdrtiGPHpG+/Nd29v/5ays6W2rYtWaKizGNEhPPdyQFvqO41pk6F2yNHSuaJO3Gi+iP7AQBqL0KZa/A5Os5qlZo2NX+ffPONdPbZVe9/773S44+bqXe+/77kditPee896YorzN9Pu3a5N7RZraaKOnWqdPCg2Xbjjeb9R0Q4fqx335Xuu89UWyWpXTtTdb3++pqHvGPHTMX23Xel5GRTTCnt7LNN0L3sMlOBDwqq2fkk857S0kyA3bmz5HHXLnOf8KkEB0tt2pQNvOWXFi3MiNbeVFhoBhkLDJRCQ027KU7VTYRbO/LyzP8YkvTnn1VPNg4AqBsIZa7B5+i4Q4dMgJBMQLLdPlWZ3FypWzdp3z4pMVH617/c30abwkJT5fz+e2n6dNd2Sa7KkSPmHtdnny35MuCxx0w34j/+MMvhwyXrpRfb9sxM81lL5vUzZpjKsO1vQleyWk3ITE42XwZ89lnZ+3FPO00aMsSE3f79TbDOzy+7nDxZcVt+vvlv5KefSsKs7T2VFxkpnXuu+X01a2aq1Pv2mfu1f//d3Ndcnb/+AwNNj0dbCLY9ll5v3dp82WG1mnafPGkq5Lb18s8LCkyV297vyd7zI0fKtsliMf+fhIZW/WhbGjQwi229ssfQUPOlQGFhyWP5pfz2/HyTLaqznDhh3ntAgPlcAwNL1ss/ll8PDDRfiJR+rGw9IMC002o1S+n18s9t6+Xf26nWbb8H22NVi22fCRPMv101Qbi1w2o1v3ir1Uw63qqVCxsJAPBLhDLX4HN03JYtUr9+pnJW3Wlm3npLGjHC/DG7c6cZ1MgTXnrJ/IHatKkZ6MnTBYLPPjPTIX39tXOvDw019yv//e+ebXtmprl/ODlZev99E9hcJSDAdNnu0aMkzPboIbVsWfXrTp40fwfbwu7vv5eE3337TBhOS6teBdjWjurui7rJFbdSVPca44KOEf7DYjHfzhw9yqBSAADAu041DZA9w4dLV14pvf22dNtt5v5Xd3fTPHbMjAosSfff752eb336mJGKFy403YmPHzdVyaZN7S/lf9ahgxQe7vl2R0SYrs/XX2+qXp9/XlLV/fZbMzBVvXqmu211ltNPN0H23HPNFxunqvbbU6+eGSirffvK9ykoMN3Bf//dhF1bCC6/npdXebC1WEreX7165guZevXMFw1V/a7KP2/SxJzjxAnze7c9ll4vv+3YsZLntvWqHk+cqLpyam97vXqmah0aah6rWkJDzfsvXx22Vy2urHJcUFD20d62oiLTRoul5NG2lH5eet3ee6tq3aZ0RdjeUnqf0oO0uVudCreS+UeAcAsAALztVNMAVWbhQjPY0saN0gsvSDff7Pq2lTZ/vqn0dehguvN6S1CQNG2auQ/XH++7DAyU+vY1y0MPebs1VQsKMl2P27SpfB+r1XQhzs8vG15ti6sHrAoJ8c4XFPAvdW6cNKYDAgAAvqA60wDZ0769NGuWWZ80SVqwwH3zrB46ZKbNkcy0PL4wGKc/BtvayGIxlenWrc18xU2aSI0amf9GGIkZ3lLn/tOzhdtjx7zbDgAAULfZKreOdEu2mTrV3ANbVGTuJZ082dxL6WoPPSTl5JhpiK691vXHBwBXqrPhlsotAADwFqvV+cqtZLqAPv+8mRrHYjGjCQ8damaDcJXvv5cWLzbrjz5KNQ6A76tz/0wRbgEAgLcdOGB6kQUGSh07OncMi0W6+27pzTelhg2ljz8286j+8kvN2nb8uDR7thQba6rBl14qXXxxzY4JAJ5Q58KtbcJzwi0AAPAWW9W2QwczCm5NXHmltHmzmVLo55+l3r3NKMqOslrNVEPdupl7ek+cMPPJvvBCzdoHAJ5S58ItlVsAAOBtzkwDVJUePaQvvpDOP9/MpTp4sJmb1pH2XHaZmUN3716pbVtp1SozKnNVI+YCgC8h3AIAAHiYs9MAVaVlS+nTT6XRo0134gkTpOnTzfyXlTl6VPrHP6Tu3aX33zdTuCQmSj/+KF1zDSMTA/AvdTbcMloyAADwlpoMJlWV+vWllSulmTPN88cek0aNknJzy+5ntZrKbNeu0ty5Zq7SSy+VvvtO+te/zJQuAOBv6my4pXILAAC8pSbTAJ1KQIAZEOo//zFzjr71ltS/v/T77+bnP/wgXXKJlJBgtnXsaPZJTnZPewDAU4K83QBPI9wCAABvKiyUfv3VrLu6clva9ddLp59u7qPduVM67zyz/sILUkGBFBpquiDfe2/J30cA4M/qXOWW0ZIBAIA3paaabsDBwVK7du49V1yc9Pnn5p7a9HQzH25BgTRypLmvduZMgi2A2qPOhVsqtwAAwJts99t26mTmuXW3Dh3MVEHXXivFxJiBo9auNdsBoDahWzIAAIAHuXoaoOoICzMDTQFAbVZnK7eMlgwAALzBHdMAAQDqcLilcgsAALzBXdMAAUBdR7gFAADwIHdOAwQAdVmdC7eMlgwAALwlP1/au9esU7kFANeqc+GWyi0AAPCWPXvMPLcNGkitW3u7NQBQuxBuAQAAPKT0/bYWi3fbAgC1TZ0Nt4yWDAAAPM0b0wABQF1RZ8MtlVsAAOBpTAMEAO5DuAUAwM8sWrRIHTt2VGhoqGJiYrRx48ZK9127dq0GDx6s5s2bKywsTHFxcfrggw/K7LN06VJZLJYKy4kTJ9z9VuocpgECAPepc+G29GjJVqt32wIAgKOSkpI0ZcoUzZgxQzt27FD//v01dOhQpaam2t1/w4YNGjx4sJKTk7V9+3YNGjRIw4YN044dO8rsFxYWprS0tDJLaGioJ95SncI0QADgPhar1fcjXnZ2tsLDw5WVlaWwsLAaHSsrSzrtNLN+4oQUElLz9gEA/JcrrzGe0Lt3b/Xq1UuLFy8u3hYdHa0RI0Zo7ty51TrGWWedpYSEBM2cOVOSqdxOmTJFR44ccbpd/vY5esPx4yVfsmdkSM2be7c9AOAvqnuNqXOVW1u3ZImuyQAA/5Kfn6/t27crPj6+zPb4+Hht2bKlWscoKipSTk6OmjZtWmZ7bm6u2rdvr7Zt2+qKK66oUNktLy8vT9nZ2WUWVO2XX8zjaadJERFebQoA1Ep1LtzWqycF/PWuGTEZAOBPMjMzVVhYqMjIyDLbIyMjlZ6eXq1jPPHEEzp69KhGjx5dvK1r165aunSp3n77ba1cuVKhoaHq16+fdtluELVj7ty5Cg8PL16ioqKce1N1CNMAAYB71blwa7EwqBQAwL9ZyiUjq9VaYZs9K1eu1KxZs5SUlKQWLVoUb+/Tp49uuOEG9ejRQ/3799eqVavUpUsXPfXUU5UeKzExUVlZWcXLvn37nH9DdQTTAAGAewV5uwHeUL++dPQo4RYA4F8iIiIUGBhYoUqbkZFRoZpbXlJSkiZMmKDXX39dl1xySZX7BgQE6LzzzquychsSEqIQBq5wCNMAAYB71bnKrUTlFgDgn4KDgxUTE6OUlJQy21NSUtS3b99KX7dy5UrddNNNWrFihS6//PJTnsdqtWrnzp1q1apVjduMEkwDBADuVScrt6WnAwIAwJ9MmzZNY8eOVWxsrOLi4vTcc88pNTVVkyZNkmS6C+/fv1/Lli2TZILtuHHjtGDBAvXp06e46lu/fn2Fh4dLkmbPnq0+ffqoc+fOys7O1sKFC7Vz504988wz3nmTtRTTAAGAe9XJcEvlFgDgrxISEnT48GHNmTNHaWlp6t69u5KTk9W+fXtJUlpaWpk5b5csWaKCggLdfvvtuv3224u333jjjVq6dKkk6ciRI7rllluUnp6u8PBw9ezZUxs2bND555/v0fdWm2VnSwcPmnUqtwDgHnVunltJ6ttX2rpVWrtWGjnSBQ0EAPgt5md1DT7Hqm3fLsXGSi1alIRcAED1MM9tFajcAgAAT+J+WwBwP8ItAACAmzENEAC4H+EWAADAzZgGCADcr06GW0ZLBgAAnkS3ZABwvzoZbqncAgAAT2IaIABwvzodbo8d8247AABA7Xf4sPTnn2b9jDO82xYAqM3qdLilcgsAANzN1iW5bduSW6MAAK5HuAUAAHAjBpMCAM8g3AIAALgR0wABgGfUyXDLaMkAAMBTqNwCgGfUyXBL5RYAAHgK0wABgGfU6XDLaMkAAMCdrFamAQIAT6nT4ZbKLQAAcKf0dOnoUSkgQDr9dG+3BgBqN8ItAACAm9i6JHfoIAUHe7UpAFDrEW4BAADchMGkAMBz6mS4ZbRkAADgCUwDBACe41S4XbRokTp27KjQ0FDFxMRo48aNVe6/fPly9ejRQw0aNFCrVq00fvx4HT582KkGuwKVWwAA4AlUbgHAcxwOt0lJSZoyZYpmzJihHTt2qH///ho6dKhSU1Pt7r9p0yaNGzdOEyZM0Pfff6/XX39dX375pSZOnFjjxjuLcAsAADyBaYAAwHMcDrfz5s3ThAkTNHHiREVHR2v+/PmKiorS4sWL7e7/2WefqUOHDrrzzjvVsWNHXXDBBbr11lu1bdu2GjfeWaWnArJavdYMAABQixUVSb/8YtbplgwA7udQuM3Pz9f27dsVHx9fZnt8fLy2bNli9zV9+/bV77//ruTkZFmtVh08eFCrV6/W5ZdfXul58vLylJ2dXWZxJVu4Nedy6aEBAAAkSfv2mb8z6tWT2rXzdmsAoPZzKNxmZmaqsLBQkZGRZbZHRkYqPT3d7mv69u2r5cuXKyEhQcHBwWrZsqVOO+00PfXUU5WeZ+7cuQoPDy9eoqKiHGnmKZUOt3RNBgAA7mDrktypkxQU5N22AEBd4NSAUhaLpcxzq9VaYZvNDz/8oDvvvFMzZ87U9u3b9f7772vPnj2aNGlSpcdPTExUVlZW8bJv3z5nmlmpevWkwECzTrgFAADuwGBSAOBZDn2PGBERocDAwApV2oyMjArVXJu5c+eqX79+uvfeeyVJ55xzjho2bKj+/fvroYceUqtWrSq8JiQkRCEhIY40zSEWi6ne5uYSbgEA8GfHj0vjxkkjR0pjxni7NWUxDRAAeJZDldvg4GDFxMQoJSWlzPaUlBT17dvX7muOHTumgICypwn8q2xq9eJoToyYDACA//voI2n1aum228yX1r6Eyi0AeJbD3ZKnTZumF154QS+99JJ+/PFHTZ06VampqcXdjBMTEzVu3Lji/YcNG6a1a9dq8eLF2r17tzZv3qw777xT559/vlq3bu26d+Kg0iMmAwAA/2QbczI7W1q50rttKY9pgADAsxwe3iAhIUGHDx/WnDlzlJaWpu7duys5OVnt27eXJKWlpZWZ8/amm25STk6Onn76ad1999067bTTdNFFF+mRRx5x3btwApVbAAD839GjJeuLF0sTJ5rbj7zt5Elp926zTrdkAPAMi9WbfYOrKTs7W+Hh4crKylJYWJhLjtmzp7Rzp7RunXTppS45JADAD7njGlMXeetzfPJJadq0kueffy6df77HTl+pXbtMqLWN8RHg1BCeAACp+teYOvtPbYMG5pHKLQAA/qt05VYy1VtfULpLMsEWADyjzv5zS7dkAAD8ny3c9u5tHl97TfrjD++1x4bBpADA8wi3hFsAAPyWLdxefLHUo4d04oT0yivebZPENEAA4A11PtwyWjIAAP7LFm4bNZL+mrhBzz4reXtEESq3AOB5dT7cUrkFAMB/2cJtw4bS9debkPu//0n//a9328U0QADgeYRbwi0AAH6rdLht3FgaO9Y8f/ZZ77XpxAnJNisi3ZIBwHPqbLhltGQAAPxf6XArSbfdZh7ffFNKS/NKk/Txx6ZbdMuWUvPm3mkDANRFdTbcUrkFAMD/lQ+3Z58t9esnFRRIL7zgnTb95z/mMSFBsli80wYAqIsIt4RbAAD8VvlwK5VUb597zoRcT8rJkd56y6zfcINnzw0AdV2dD7eMlgwAgP+yF25HjZKaNZN+/1167z3PtueNN8wX52eeKcXEePbcAFDX1flwS+UWAAD/ZS/choZKf/ubWff0wFK2LsnXX0+XZADwNMIt4RYAAL+Vm2seS4dbSbr1VvP4wQfS7t2eacuBA2YwKcmEWwCAZ9XZcMtoyQAA+LeiopLrePlw26mTNGSIGbV4yRLPtOe110yb+vaVTj/dM+cEAJSos+GWyi0AAP6t9LgZ5cOtVDKw1EsvSXl57m/P8uXmkYGkAMA7CLeEWwAA/JLtflup5Lpe2uWXS23bSpmZ0urV7m3LDz9IX30lBQVJ11zj3nMBAOyr8+GW0ZIBAPBPtnDboIEUYOcvmqAg6eabzfrixe5ti61qO3SoFBHh3nMBAOyr8+GWyi0AAP7J3kjJ5U2cKAUGSps3S99+6552FBXRJRkAfAHhlnALAIBfqk64bd1aGjHCrLtrWqDNm6XffpMaN5aGDXPPOQAAp1Znwy2jJQMA4N9s4bZRo6r3sw0s9eqrJVMHuZKtajtqlP17fwEAnlFnw23pyq3V6t22AAAAx1WncitJF10kdeki5eSUBFFXycuTVq0y63RJBgDvqvPhVvLM9AAAAMC1qhtuLRbp1lvN+uLFrv1Se9066c8/TffngQNdd1wAgOMIt2LEZAAA/FF1w60k3XSTFBoqff219NlnrmvDf/5jHseMMQNXAQC8p86G23r1Si5C3HcLAID/cSTcNm0qJSSYdVcNLHXkiPTuu2b9+utdc0wAgPPqbLiVGDEZAAB/5ki4lUoGlkpKkg4frvn516wxtzaddZbUo0fNjwcAqJk6HW4ZMRkAAP/laLg9/3ypZ08TSJcurfn5bV2Sb7jB3NcLAPCuOh1uqdwCAOC/HA23FktJ9fbZZ6WiIufPvW+f9OmnZn3MGOePAwBwHcKtCLcAAP+yaNEidezYUaGhoYqJidHGjRsr3Xft2rUaPHiwmjdvrrCwMMXFxemDDz6osN+aNWvUrVs3hYSEqFu3bnrjjTfc+RZcwjZnbXXDrSRdd50UFib98ot0//3On3vlSvM4YIDUrp3zxwEAuA7hVoyWDADwH0lJSZoyZYpmzJihHTt2qH///ho6dKhSU1Pt7r9hwwYNHjxYycnJ2r59uwYNGqRhw4Zpx44dxfts3bpVCQkJGjt2rL7++muNHTtWo0eP1ueff+6pt+UURyu3ktSokfTUU2Z97lzpueecO3fpLskAAN9gsVpdOdube2RnZys8PFxZWVkKCwtz2XH79ZO2bDEDQlx1lcsOCwDwI+66xrhL79691atXLy1evLh4W3R0tEaMGKG5c+dW6xhnnXWWEhISNHPmTElSQkKCsrOztW7duuJ9Lr30UjVp0kQrbSXKcvLy8pRXaqL47OxsRUVFefRzvPJK6Z13TEC9+WbHXjtrljR7tpk54d13pUsvrf5rv/nGDCAVHCylp0tNmjh2bgCAY6p7raZyK7olAwD8Q35+vrZv3674+Pgy2+Pj47Vly5ZqHaOoqEg5OTlq2rRp8batW7dWOOaQIUOqPObcuXMVHh5evERFRTnwTlzDmcqtzYMPSuPGSYWF0jXXSDt3Vv+1tqrtFVcQbAHAl9TpcMtoyQAAf5KZmanCwkJFRkaW2R4ZGan09PRqHeOJJ57Q0aNHNXr06OJt6enpDh8zMTFRWVlZxcu+ffsceCeuUZNwa7FIzz8vXXSRuXf38svNIFGnUlQkrVhh1pnbFgB8S50Ot1RuAQD+yFJu3hmr1Vphmz0rV67UrFmzlJSUpBYtWtTomCEhIQoLCyuzeFpNwq1kuhWvWWPmqT1wwATcrKyqX7N+vbR/v3TaadJllzl3XgCAexBuRbgFAPiHiIgIBQYGVqioZmRkVKi8lpeUlKQJEyZo1apVuuSSS8r8rGXLlk4d09tqGm4lE1Lfe09q2VL69lvTRfnkycr3t3VJvuYaKTTU+fMCAFyPcCtGSwYA+Ifg4GDFxMQoJSWlzPaUlBT17du30tetXLlSN910k1asWKHLL7+8ws/j4uIqHPPDDz+s8pi+wBZuGzWq2XHatzcBt2FDKSVFuvVWyd5wmydOSKtXm3VGSQYA3xPk7QZ4E5VbAIC/mTZtmsaOHavY2FjFxcXpueeeU2pqqiZNmiTJ3Au7f/9+LVu2TJIJtuPGjdOCBQvUp0+f4gpt/fr1FR4eLkm66667NGDAAD3yyCMaPny43nrrLX300UfatGmTd95kNbmicmvTq5e0apU0bJj08stSx47SAw+U3efdd6XsbCkqSrrggpqfEwDgWlRuRbgFAPiPhIQEzZ8/X3PmzNG5556rDRs2KDk5We3bt5ckpaWllZnzdsmSJSooKNDtt9+uVq1aFS933XVX8T59+/bVa6+9ppdfflnnnHOOli5dqqSkJPXu3dvj76+6rNaSnleuCLeSuYf2mWfM+syZ0quvlv25rUvy9ddLAXX6LygA8E11ep7bhx4y38pOnGhGTAQA1D3+Ns+tr/L053jsWEmozcmpedfk0qZPlx57TKpXT/rgA2nQIOnwYalVK3M/7nffmUGoAACewTy31cBUQAAA+Cdbl2Sp5HruKv/+tzR6tAmyI0dKP/wgvf66eX7uuQRbAPBV3HMrwi0AAP7GFm7r13d9F+GAAOmVV8yUP5s3m+7KTZqYnzG3LQD4rjpduWW0ZAAA/JMrB5OyJzRUeustqXNn6bffpJ07JYtFuu4695wPAFBzhFtRuQUAwN/k5ppHd4VbSWrWTFq3ToqIMM8vukhq08Z95wMA1AzhVoRbAAD8jbsrtzadOpmAe8UV0sMPu/dcAICa4Z5bEW4BAPA3ngq3khQbK73zjvvPAwComTpduWW0ZAAA/JMnwy0AwD/U6XBL5RYAAP9EuAUAlEe4FaMlAwDgbwi3AIDyCLeicgsAgL8h3AIAyiPcSjpxQrJavdsWAABQfYRbAEB5hNu/nDjhvXYAAADH2MJto0bebQcAwHcQbv9C12QAAPwHlVsAQHl1OtzWqycF/TXTL+EWAAD/QbgFAJRXp8OtxIjJAAD4I8ItAKA8wi0jJgMA4HcItwCA8gi3hFsAAPwO4RYAUB7hlnALAIDfIdwCAMqr8+G2QQPzSLgFAMB/5OaaR8ItAMCmzodbKrcAAPgfKrcAgPIIt4yWDACA3yHcAgDKI9xSuQUAwK9YrYRbAEBFhFvCLQAAfuXECRNwJcItAKAE4ZZwCwCAX7FVbSXCLQCghFPhdtGiRerYsaNCQ0MVExOjjRs3Vrl/Xl6eZsyYofbt2yskJESdOnXSSy+95FSDXY3RkgEA8C+2cBsSIgUGerctAADfEeToC5KSkjRlyhQtWrRI/fr105IlSzR06FD98MMPateund3XjB49WgcPHtSLL76oM844QxkZGSooKKhx412Byi0AAP6F+20BAPY4HG7nzZunCRMmaOLEiZKk+fPn64MPPtDixYs1d+7cCvu///77Wr9+vXbv3q2mTZtKkjp06FCzVrsQoyUDAOBfbOG2USPvtgMA4Fsc6pacn5+v7du3Kz4+vsz2+Ph4bdmyxe5r3n77bcXGxurRRx9VmzZt1KVLF91zzz06XkWpNC8vT9nZ2WUWd6FyCwCAf6FyCwCwx6HKbWZmpgoLCxUZGVlme2RkpNLT0+2+Zvfu3dq0aZNCQ0P1xhtvKDMzU5MnT9Yff/xR6X23c+fO1ezZsx1pmtMItwAA+BfCLQDAHqcGlLJYLGWeW63WCttsioqKZLFYtHz5cp1//vm67LLLNG/ePC1durTS6m1iYqKysrKKl3379jnTzGoh3AIA4F8ItwAAexyq3EZERCgwMLBClTYjI6NCNdemVatWatOmjcLDw4u3RUdHy2q16vfff1fnzp0rvCYkJEQhISGONM1pjJYMAIB/IdwCAOxxqHIbHBysmJgYpaSklNmekpKivn372n1Nv379dODAAeXm5hZv+9///qeAgAC1bdvWiSa7FpVbAAD8C+EWAGCPw92Sp02bphdeeEEvvfSSfvzxR02dOlWpqamaNGmSJNOleNy4ccX7jxkzRs2aNdP48eP1ww8/aMOGDbr33nv1t7/9TfVtydKLGC0ZAAD/Yvu+nHALACjN4amAEhISdPjwYc2ZM0dpaWnq3r27kpOT1b59e0lSWlqaUlNTi/dv1KiRUlJSdMcddyg2NlbNmjXT6NGj9dBDD7nuXdQAlVsAAPwLlVsAgD0Oh1tJmjx5siZPnmz3Z0uXLq2wrWvXrhW6MvsKwi0AAP6FcAsAsMep0ZJrE8ItAAD+hXALALCnzodbRksGAMC/EG4BAPbU+XBL5RYAAP9CuAUA2EO4/SvcnjghFRV5ty0AAODUCLcAAHsIt6VmIzpxwnvtAAAA1UO4BQDYQ7gtFW7pmgwAgO+zhdtGjbzbDgCAb6nz4TYoyCwS4RYAAH9A5RYAYE+dD7cSIyYDAOBPCLcAAHsIt2LEZAAA/AnhFgBgD+FWhFsAAPyF1Uq4BQDYR7hVSbg9dsy77QAAAFXLz5cKC8064RYAUBrhVlRuAQDwF7aqrUS4BQCURbgV4RYAAH+Rm2seg4NLZjsAAEAi3EpitGQAAPwF99sCACpDuBWVWwAA/AXhFgBQGcKtCLcAAPgLwi0AoDKEWzFaMgAA/oJwCwCoDOFWVG4BAP5l0aJF6tixo0JDQxUTE6ONGzdWum9aWprGjBmjM888UwEBAZoyZUqFfZYuXSqLxVJhOXHihBvfhXMItwCAyhBuRbgFAPiPpKQkTZkyRTNmzNCOHTvUv39/DR06VKmpqXb3z8vLU/PmzTVjxgz16NGj0uOGhYUpLS2tzBIaGuqut+E0wi0AoDKEWzFaMgDAf8ybN08TJkzQxIkTFR0drfnz5ysqKkqLFy+2u3+HDh20YMECjRs3TuHh4ZUe12KxqGXLlmUWX0S4BQBUhnArKrcAAP+Qn5+v7du3Kz4+vsz2+Ph4bdmypUbHzs3NVfv27dW2bVtdccUV2rFjR5X75+XlKTs7u8ziCbZw26iRR04HAPAjhFsRbgEA/iEzM1OFhYWKjIwssz0yMlLp6elOH7dr165aunSp3n77ba1cuVKhoaHq16+fdu3aVelr5s6dq/Dw8OIlKirK6fM7gsotAKAyhFsxWjIAwL9YLJYyz61Wa4VtjujTp49uuOEG9ejRQ/3799eqVavUpUsXPfXUU5W+JjExUVlZWcXLvn37nD6/Iwi3AIDKBHm7Ab6Ayi0AwB9EREQoMDCwQpU2IyOjQjW3JgICAnTeeedVWbkNCQlRSEiIy85ZXYRbAEBlqNyKcAsA8A/BwcGKiYlRSkpKme0pKSnq27evy85jtVq1c+dOtWrVymXHdBXCLQCgMlRuxWjJAAD/MW3aNI0dO1axsbGKi4vTc889p9TUVE2aNEmS6S68f/9+LVu2rPg1O3fulGQGjTp06JB27typ4OBgdevWTZI0e/Zs9enTR507d1Z2drYWLlyonTt36plnnvH4+zsVwi0AoDKEW1G5BQD4j4SEBB0+fFhz5sxRWlqaunfvruTkZLVv316SlJaWVmHO2549exavb9++XStWrFD79u21d+9eSdKRI0d0yy23KD09XeHh4erZs6c2bNig888/32Pvq7pyc80j4RYAUJ7FarVavd2IU8nOzlZ4eLiysrIUFhbm8uNv2SL16yd16iT98ovLDw8A8GHuvsbUFZ76HC+4QNq8WVq9Who1ym2nAQD4kOpeY7jnVoyWDACAv6BbMgCgMoRb0S0ZAAB/QbgFAFSGcCvCLQAA/oJwCwCoDOFWJeE2L08qKvJuWwAAQOUItwCAyhBuVTIVkCSdOOG9dgAAgKoRbgEAlSHcqqRyK9E1GQAAX5WfLxUUmHXCLQCgPMKtpMBAqV49s86IyQAA+CZb1VYi3AIAKiLc/oVBpQAA8G22cFuvnhQc7N22AAB8D+H2L4RbAAB8G/fbAgCqQrj9C+EWAADfRrgFAFSFcPsX24jJhFsAAHwT4RYAUBXC7V+o3AIA4NsItwCAqhBu/2ILt4yWDACAbyLcAgCqQrj9C5VbAAB8W26ueSTcAgDsIdz+hXALAIBvo3ILAKgK4fYvhFsAAHwb4RYAUBXC7V8YLRkAAN9GuAUAVIVw+xcqtwAA+DbCLQCgKoTbvzBaMgAAvo1wCwCoCuH2L1RuAQDwbYRbAEBVCLd/IdwCAODbCLcAgKoQbv9CuAUAwLcRbgEAVSHc/oXRkgEA8G22cNuokXfbAQDwTYTbv1C5BQDAt1G5BQBUhXD7F0ZLBgDAtxFuAQBVIdz+hcotAAC+jXALAKgK4fYvhFsAAHwb4RYAUBXC7V8ItwAA+LbcXPNIuAUA2EO4/QujJQMA4LtOnjSLRLgFANhHuP0LlVsAAHyXrUuyRLgFANhHuP0LoyUDAOC7bOE2MFAKDvZuWwAAvolw+xdbuM3PlwoLvdsWAABQVunBpCwW77YFAOCbCLd/sYVbSTpxwnvtOBWrVbr8cunCCwnhAIC6g5GSAQCn4lS4XbRokTp27KjQ0FDFxMRo48aN1Xrd5s2bFRQUpHPPPdeZ07pV6XDry/fd/vmnlJwsbdggpaZ6uzUAAHgG4RYAcCoOh9ukpCRNmTJFM2bM0I4dO9S/f38NHTpUqadIWllZWRo3bpwuvvhipxvrTqXv4fHlcHvgQMn6vn3eawcAAJ5EuAUAnIrD4XbevHmaMGGCJk6cqOjoaM2fP19RUVFavHhxla+79dZbNWbMGMXFxTndWHfzhxGTCbcAgLqIcAsAOBWHwm1+fr62b9+u+Pj4Mtvj4+O1ZcuWSl/38ssv69dff9WDDz5YrfPk5eUpOzu7zOIJ/jBi8v79JeuEWwBAXUG4BQCcikPhNjMzU4WFhYqMjCyzPTIyUunp6XZfs2vXLt13331avny5goKCqnWeuXPnKjw8vHiJiopypJlO87fKLffcAgDqClu4bdTIu+0AAPgupwaUspQbg99qtVbYJkmFhYUaM2aMZs+erS5dulT7+ImJicrKyipe9nmoROkP4ZbKLQCgLqJyCwA4leqVUv8SERGhwMDAClXajIyMCtVcScrJydG2bdu0Y8cO/d///Z8kqaioSFarVUFBQfrwww910UUXVXhdSEiIQkJCHGmaS/hDuOWeWwBAXUS4BQCcikOV2+DgYMXExCglJaXM9pSUFPXt27fC/mFhYfr222+1c+fO4mXSpEk688wztXPnTvXu3btmrXexBg3Moy+H29KVW7olAwDqCsItAOBUHKrcStK0adM0duxYxcbGKi4uTs8995xSU1M1adIkSaZL8f79+7Vs2TIFBASoe/fuZV7fokULhYaGVtjuC/ytcvvnn+Ziz4UeAFDbEW4BAKficLhNSEjQ4cOHNWfOHKWlpal79+5KTk5W+/btJUlpaWmnnPPWV/n6aMkFBZKtR3hAgFRUZLomd+3q3XYBAOBuubnmkXALAKiMUwNKTZ48WXv37lVeXp62b9+uAQMGFP9s6dKl+vTTTyt97axZs7Rz505nTut2vl65zcgwgTYwUDrzTLPNT79HAADAIVRuAQCn4lS4ra18Pdza7rdt2VLq0MGsM6gUAKAuINwCAE6FcFuKr4db2/22rVtLtql/CbcAgLqAcAsAOBXCbSm+PlqyrXLbpo3Urp1Zp1syAKAuINwCAE6FcFsKlVsAAHwT4RYAcCqE21J8fbTk0pVbwi0AoC4h3AIAToVwW4o/VW5Ld0u2Wr3XJgAAPIFwCwA4FcJtKb4ebktXbtu2NevHj0t//OG9NgEA4AmEWwDAqRBuS/H1cFu6chsSIrVoYZ7TNRkAUJsVFkp5eWadcAsAqAzhthRfHi35+HHpzz/Neps25pH7bgGgblq0aJE6duyo0NBQxcTEaOPGjZXum5aWpjFjxujMM89UQECApkyZYne/NWvWqFu3bgoJCVG3bt30xhtvuKn1jrNVbSWpUSPvtQMA4NsIt6X4cuXWVrWtX18KDzfrTAcEAHVPUlKSpkyZohkzZmjHjh3q37+/hg4dqtRKLgZ5eXlq3ry5ZsyYoR49etjdZ+vWrUpISNDYsWP19ddfa+zYsRo9erQ+//xzd76VarOF24AA03MJAAB7CLel+PJoyaXvt7VYzDqVWwCoe+bNm6cJEyZo4sSJio6O1vz58xUVFaXFixfb3b9Dhw5asGCBxo0bp3Dbt6PlzJ8/X4MHD1ZiYqK6du2qxMREXXzxxZo/f74b30n1lb7f1nYNBACgPMJtKf5QuW3dumQb4RYA6pb8/Hxt375d8fHxZbbHx8dry5YtTh9369atFY45ZMiQKo+Zl5en7OzsMou7MJgUAKA6CLel+HK4LV25taFbMgDULZmZmSosLFRkZGSZ7ZGRkUpPT3f6uOnp6Q4fc+7cuQoPDy9eomzfuLpBbq55JNwCAKpCuC3Fl8MtlVsAgI2lXN9cq9VaYZu7j5mYmKisrKziZZ8bL0ZUbgEA1RHk7Qb4El8eLdkWbktXbm3hdv9+M01CYKDn2wUA8JyIiAgFBgZWqKhmZGRUqLw6omXLlg4fMyQkRCEeGt2JcAsAqA4qt6XYKrf5+SYs+hJbt+TSldtWrUygLSiQatAbDQDgJ4KDgxUTE6OUlJQy21NSUtS3b1+njxsXF1fhmB9++GGNjulKhFsAQHVQuS3FFm4lU731pbn07FVuAwPN89RU0zW59M8AALXTtGnTNHbsWMXGxiouLk7PPfecUlNTNWnSJEmmu/D+/fu1bNmy4tfs3LlTkpSbm6tDhw5p586dCg4OVrdu3SRJd911lwYMGKBHHnlEw4cP11tvvaWPPvpImzZt8vj7s4dwCwCoDsJtKb4abq1W+5VbyXRNtoXbPn083zYAgGclJCTo8OHDmjNnjtLS0tS9e3clJyerffv2kqS0tLQKc9727NmzeH379u1asWKF2rdvr71790qS+vbtq9dee03333+/HnjgAXXq1ElJSUnq3bu3x95XVQi3AIDqINyWEhAgBQebbsm+dN/tkSPSiRNmvXy4bddO2ryZEZMBoC6ZPHmyJk+ebPdnS5curbDNarWe8phXX321rr766po2zS0ItwCA6uCe23J8ccRkW9W2aVMpNLTszxgxGQBQ2xFuAQDVQbgtxxdHTLZ3v60N4RYAUNsRbgEA1UG4LceXK7fluyRLJeGWbskAgNqKcAsAqA7CbTm+GG6rqty2a2ceqdwCAGorwi0AoDoIt+XYwu2xY95tR2nVqdwePCjl5XmuTQAAeIot3PrKLAYAAN9EuC3H3yq3zZqVDDJlC8EAANQmVG4BANVBuC3HF8NtVZVbi6WkazL33QIAaiPCLQCgOgi35fjbaMkSIyYDAGo3wi0AoDoIt+X4WuW2oEBKTzfr9iq3EuEWAFC75eaaR8ItAKAqhNtyfC3cZmRIRUVSYKDUooX9feiWDACozajcAgCqg3Bbjq+Nlmy737ZlSxNw7aFyCwCozQi3AIDqINyW42uVW9v9tpV1SZYItwCA2quwUDpxwqwTbgEAVSHcluNr4dZWua1sMCmJbskAgNqrdE8qwi0AoCqE23J8Ldw6UrnNypJyctzfJgAAPMXWJdliKblGAwBgD+G2HF+bCqg6ldtGjaTTTjPrdE0GANQmtnDboIEJuAAAVIZwW44/Vm6lkuotXZMBALUJg0kBAKqLcFuOr46WXFXlViq575bKLQCgNiHcAgCqi3Bbjr9Xbgm3AIDahHALAKguwm05vhRujx+X/vzTrJ+qcku4BQDURrZw26iRd9sBAPB9hNtyfCnc2qq29etL4eFV78t0QACA2ojKLQCgugi35fjSaMm2cNumzalHiKRyCwCojQi3AIDqItyW40uVW9tgUqe631YqG26tVve1CQAATyLcAgCqi3Bbji+Nlly6cnsqturuiRNSZqZ72wUAgKfk5ppHwi0A4FQIt+X4a+U2JESKjDTrdE0GANQWVG4BANVFuC3HFm5PnpQKC73bFkcqtxL33QIAah/CLQCgugi35djCreT96q0jlVuJEZMBALUP4RYAUF2E23J8KdzW9sptTg6DXwEAqka4BQBUF+G2nIAAc/+q5N1wa7U6Xrn1p3CbkmLm7v3nP73dEgCALyPcAgCqi3Brhy+MmHzkiBn5WHI83PpDt+QXXzQB/qmnzP3NAADYQ7gFAFQX4dYOXxgx2Va1bdpUCg2t3mts99z6euX25Enp/ffNemam9NFH3m0PAMB3EW4BANVFuLXDF8Kto/fbSiWV2wMHpIIC17fJVTZtkrKySp6vWOG9tgAAfBvhFgBQXYRbO3wh3Dp6v61k5rkNCjJTGKWluaddrvDOO+axe3fz+MYb3u0CDgDwXYRbAEB1EW7taNDAPPpb5TYwUGrb1qz7atdkq7Uk3D74oNSxo/nDxbYNAIDSCLcAgOoi3Nrhr5VbyfdHTP7f/6RffpGCg6UhQ6QxY8z25cu92y4AgG+yhdtGjbzbDgCA7yPc2uELoyU7U7mVfD/c2iq0AwdKjRuXhNt166TDh73WLACADyoqKrkWU7kFAJwK4dYOf67c2kZM9tXpgN591zxecYV57NZN6tHDDIC1Zo332gUA8D2lr8OEWwDAqRBu7fCFcFsbK7d//mlGSpZKwq1UUr1l1GQAQGm2LslSybUZAIDKEG7t8Ha4LSiQ0tPNem2653bdOjOS81lnmYGkbK691jxu2OCb7QYAeEdurnls0EAK4C8WAMApcKmww9ujJWdkmPuMAgOlFi0ce60vd0u2dUkeNqzs9nbtpAEDzEjKSUmebxcAwDcxUjIAwBGEWzu8Xbm13W/bsqUJuI6wVW4PHZJOnHBtu2ri5ElTuZXKdkm2YdRkAEB5hFsAgCMIt3Z4e7RkZ++3laQmTUoqz7//7ro21dSWLdKRI1JEhNSnT8WfX321FBQk7dwp/fCDp1sHAPBFhFsAgCOcCreLFi1Sx44dFRoaqpiYGG3cuLHSfdeuXavBgwerefPmCgsLU1xcnD744AOnG+wJ3q7c2sKto/fbSpLFUlK99aWuybYpgC67zH41ulkz6dJLzfrKlZ5rFwDAdxFuAQCOcDjcJiUlacqUKZoxY4Z27Nih/v37a+jQoUqtJElt2LBBgwcPVnJysrZv365BgwZp2LBh2rFjR40b7y7eDre2bsnOVG6lkvtufWlwpvJTANlTetRkq9X9bQIA+DbCLQDAEQ6H23nz5mnChAmaOHGioqOjNX/+fEVFRWnx4sV2958/f76mT5+u8847T507d9a//vUvde7cWe/YSnk+yNvhtiaVW8n3RkzetUv6+WfT7XjIkMr3u/JK8wfM7t3SF194rn0AAN9EuAUAOMKhcJufn6/t27crPj6+zPb4+Hht2bKlWscoKipSTk6OmjZtWuk+eXl5ys7OLrN4krdHS7ZVbmsabn2lW7Lte4wLL5TCwirfr2FDacQIs+4vA0tt2yY995wZMAsA4FqEWwCAIxwKt5mZmSosLFRkZGSZ7ZGRkUq3Tcx6Ck888YSOHj2q0aNHV7rP3LlzFR4eXrxE2dKah/hK5ba2dEuubAoge2xdk5OSzHy/vmzrVql/f+nWW81cvfn53m4RANQuhFsAgCOcGlDKYrGUeW61Witss2flypWaNWuWkpKS1KKKCVwTExOVlZVVvOzzcErz9mjJrqrc+kK4PXJEso03VtX9tjaDB5vBpTIypP/+161Nq5H//c+Eddt0S2vXSldd5VvTLwGAvyPcAgAc4VC4jYiIUGBgYIUqbUZGRoVqbnlJSUmaMGGCVq1apUsuuaTKfUNCQhQWFlZm8SRvVm6PH5f+/NOsO1u59aVw+8EHpgIbHS116nTq/evVk2xF/RUr3Ns2Z2VkSEOHSocPS7GxJtiGhkrvvWfuG/bWlyIAUNsQbgEAjnAo3AYHBysmJkYpKSlltqekpKhv376Vvm7lypW66aabtGLFCl1++eXOtdSDvBlubV2S69eXwsOdO4Yt3GZnS1lZrmmXs2z321anS7LN9debx7Vrvdc1vDJHj5oK9O7d0umnmy7XI0dK69aZP75SUsx0Rzk53m4pAPg/W7ht1Mi77QAA+AeHuyVPmzZNL7zwgl566SX9+OOPmjp1qlJTUzVp0iRJpkvxuHHjivdfuXKlxo0bpyeeeEJ9+vRRenq60tPTleXt1FUFXwi3bdqYOWud0bChZBuvy5vV24ICKTnZrFenS7JNXJzUvr0JiLb7dX1BQYG5t/bLL03X6XXrJFuHhYEDpQ8/NANmrV9vRoX24f/EAcAvULkFADjC4XCbkJCg+fPna86cOTr33HO1YcMGJScnq3379pKktLS0MnPeLlmyRAUFBbr99tvVqlWr4uWuu+5y3btwMW+OllzT+21tfKFr8tatpot106YmsFZXQIB03XVm3Ve6Jlut0h13mLAdGmoq0l26lN2nb1/po4+kJk3Me7/kEumPP7zTXkdYraYS7Q9tBVC3EG4BAI5wakCpyZMna+/evcrLy9P27ds1YMCA4p8tXbpUn376afHzTz/9VFartcKydOnSmrbdbXylclsTthGTvTkdkK1L8mWXmTluHWEbNTk5ueQeZG965BHp2WdNNX3FisrD+nnnSZ98IkVEmGmCBg0y9+j6osxMacECqWdPcz90ZKT5Xb3yClVnAL4hN9c8Em4BANXhVLit7Wzh9uRJz09HU5sqt7YuxY50SbY5+2yz5Oebe2+9aflyKTHRrC9YYO6xrUqPHqZrcsuW0jffmC7LaWlub2a1FBSYga+uvtr8NzZlivT111JgoPnZunXSTTdJLVpIw4dLK1eW/HEJAJ5G5RYA4AjCrR22cCt5vnrrqsqtt8Ptr79KP/5oKrZDhjh3DFv11ptdk//7X2n8eLN+zz2ma3J1dOtmAm7btuZzGDDAu180/PyzdN99pqJ/xRXSmjXmy5uYGOmZZ6RDh0w7Z882I1vn50tvv21+By1aSNdcI61e7TsjQefkmC8O3npLmj9fuusuM1J1374muN99t7Rwofn5zp1mSiqgNlm0aJE6duyo0NBQxcTEaKNtzrVKrF+/XjExMQoNDdXpp5+uZ599tszPly5dKovFUmE54eX5zQi3AABHONhZtG4IDS1ZP35catzYc+d2deXWW92SbV2S+/eXTjvNuWNce62pmH7yiflcahr4HfXtt6ZKe/KklJBguiY7oksXacMG6aKLpF9+MQH3v/+VOnZ0T3vLy8mRVq2SXnpJ2rKlZHtEhHTDDSa0n3NOyfYmTaSZM6UHHpC++05KSjLLL7+YYLt6tfkD88orzedx6aVSSIh72p6XJ/32m7Rnj/3l8GHHjxkWZgYqK780a2ZGYm3UyPy/blsPDnbs+Pn5psqdk2MW23purlmOHq34aG9bbq7p/t6wYfWXBg3MNFqVLUFBFbcVFpb0Tjl58tRLQYHjS2CgOXfpxd422xIQUPlisdjf3rVrzf+99DdJSUmaMmWKFi1apH79+mnJkiUaOnSofvjhB7Wz3ZNSyp49e3TZZZfp5ptv1n/+8x9t3rxZkydPVvPmzTVq1Kji/cLCwvTzzz+XeW1o6QuiFxBuAQCOINzaERBg/mjPy/Pfyq3t7xtvVQttXZIdmQKovA4dpH79pM2bTciaNs0lTauW3383c9lmZ5tQ+sor5r8LR3XsWDHgfvxxxcGoXCU/33wZsHKl9PrrJZXWgABzP+348aZyW1Vws1hKuoX/85/Sjh0lQfe338yxV640YaRNG/NFSlSUqVLb1m3Pmze3/7mdPGk+4z17pL17Kz4eOGAGuqpK06bm8y29RESY1/72W9klM9P8Lr/91izVUa9eSdAtvQQFlQ2vtvX8/OodF671wgvShAneboVnzZs3TxMmTNDEiRMlSfPnz9cHH3ygxYsXa+7cuRX2f/bZZ9WuXTvNnz9fkhQdHa1t27bp8ccfLxNuLRaLWrZs6ZH3UF2EWwCAIwi3lWjQwPPh1mp1feX299+loiLngpmzsrJMl1ypZuFWMt1iN282XZM9FW6zskwQ3L/fdNF9882aVSijokzAvfhi0/X3wgulxx4z9+K2bVvz9h4/bqYhWrPGVMxLd8E980zpb3+Txo6VWrVy/NgWi9Srl1n+/W/piy9MyF21ynw+tvBYmeBg8x7btjX3IB88aMLrvn3mv8uqNGhQMbyWXsLCqv8+jh415ywfelNTzedVusJq64V58qQZzMzRAc1CQkwFuHQV2LY0bHjqR9sf8bbKrr2ldOX36FHzJUZVVVh72wMDq67sVlb9razqaq9CW1RU/SrvyZNmf6vVPNpb7P2sSRPHfj/+Lj8/X9u3b9d9991XZnt8fLy2lO6iUcrWrVsVHx9fZtuQIUP04osv6uTJk6pXr54kKTc3V+3bt1dhYaHOPfdc/fOf/1TPnj0rbUteXp7y8vKKn2dnZzv7tipFuAUAOIJwW4n69c0ftZ4Mt0eOlPxhXdNwa5snNy/P3E9pm4/VEz780PyxeuaZ0hln1OxYo0eb+ym3bzf3jZ55pmvaWJn8fGnUKFPda9nSDLDkij+eW7WSPv1UGjzY3Cs6dqzZ3rGjCbu2pUOH6s1vnJNjRpJes8Y82v4AlMzveuRIadw4qU8f5+dLLs9ikXr3Nsvjj5twu2+f+QJl376K6+np5vPcvdss5YWEmPfboYP5HMo/RkS4ru0NG5ruq127nnrfgoKSAFm+a3Furvl56S7M5YPsXzkBcIvMzEwVFhYqstw/6pGRkUpPT7f7mvT0dLv7FxQUKDMzU61atVLXrl21dOlSnX322crOztaCBQvUr18/ff311+rcubPd486dO1ezZ892zRuzw2ot6X1CuAUAVAfhthK2QaU8OYCOrWrbtGnZ+36dUa+eCVQHDpig4clwa7vftqZVW8kEnPh4E+BWrpRmzar5MStTWGi6N378sQkpycnmnkxXadHCdBl+7DFzjq++KrmH1DYzVtu2JUF3wADTfdkW8P780wzytGaN+QKhVMFE7dpJV11llr59TdXMnQICSrofV+bkSfPfny3wpqebz8AWXiMjPdujoLqCgqTwcLMAvspS7psfq9VaYdup9i+9vU+fPurTp0/xz/v166devXrpqaee0sKFC+0eMzExUdNKdanJzs5WVFX/KDjo+PGS2xMItwCA6iDcVsIbc9266n5bm6ioknAbG+uaY55KYaEJhZJzUwDZM2aMOeaKFdKDD7qumldaZqZ0/fUmNAYGmsGTquiN57SmTSXbLXE5OWagp/XrzfLllyYILl9uFslUjwcMMMH2k0/KTk3VubOpMo8aZUY9dsfnUhP16pUM2gTANSIiIhQYGFihSpuRkVGhOmvTsmVLu/sHBQWpWbNmdl8TEBCg8847T7t27aq0LSEhIQpx16hyKtsjpUEDt50GAFCLEG4r4Y1w66r7bW2ioqTPP/fsiMmffWZGsj3tNDMYlCsMH25+H7t2Sdu2Seed55rj2nzxhZk+Zt8+c55XXnF++iJHNG5szmM717Fj0tat5v7c9evNZ5mebu5vtTnnHFOdHTVKOuss3wu0ANwrODhYMTExSklJ0chSk26npKRo+PDhdl8TFxend2xdav7y4YcfKjY2tvh+2/KsVqt27typs88+23WNd5At3IaGur83CgCgdiDcVqI2VG69MWKy7e+noUNN905XaNTIBNzXXjPVW1eFW6tVWrLE3NObn28qoWvWmFGCvaFBAzPo1MUXm+cnTpjgvWmTuT/1yitNGwHUbdOmTdPYsWMVGxuruLg4Pffcc0pNTdWkSZMkme7C+/fv17JlyyRJkyZN0tNPP61p06bp5ptv1tatW/Xiiy9q5cqVxcecPXu2+vTpo86dOys7O1sLFy7Uzp079cwzz3jlPUoMJgUAcBzhthK2LlD+XrmVPBtuXTEFkD3XX2/C7bJlZkCja66p2Tf5x45JkyZJr75qno8cKb38sm/dZxkaarokDxjg7ZYA8CUJCQk6fPiw5syZo7S0NHXv3l3Jyclq/9c9AGlpaUot1WWnY8eOSk5O1tSpU/XMM8+odevWWrhwYZlpgI4cOaJbbrlF6enpCg8PV8+ePbVhwwadf/75Hn9/NoRbAICjCLeVqA2VW0+H2z17pO+/N6Hz0ktde+z4eDPS7U8/SdddZwaWuv9+6dprHa8Q79pVMiJyYKCZ4ubuu+niC8B/TJ48WZMnT7b7s6W2EepKufDCC/XVV19Verwnn3xSTz75pKua5xK2cNuokXfbAQDwHz44Vqlv8MZoybZw66rKra1bsqfuubV1Sb7gAtfPPRkcbO5B/ec/zbF//tlMpxMdbUYaPnmyesd56y0zuNa335rRej/+WLrnHoItAPgaKrcAAEcRbivhzQGlXF25TUsrO8quu7irS7JNeLip1u7dK/3rX1KzZtIvv0jjx5uq7osvmntn7SkokO67TxoxQsrONoNdffWVmXIHAOB7CLcAAEcRbivh6XBbWGhGxpVcV7lt0cJMx1JUVFIVdpfsbOnTT826q6YAqkxYmJSYaELuo49KzZtLu3dLEyeaeWGXLCk7B+zBg6Zb8yOPmOdTpphpdVz1OQMAXC831zwSbgEA1UW4rYSnw+3BgyaEBgaaUOoKAQFS27Zm3d1dk1NSTNfgzp2lM89077lsGjWS7r3XhNx588ycsL/9ZgaKOuMM6ZlnTIjt1cs8NmwoJSVJTz5pQj8AwHdRuQUAOIpwWwlPj5Zsq6y2bOna+fw8NR2Q7X5bd3VJrkqDBtLUqaZ6u3Chqcj+/rv0f/8nXXSR+Wy7dpW+/FIaPdrz7QMAOI5wCwBwFOG2Ep6u3Lr6flsbT4yY/PPPkm26xOHD3XeeU6lfX7rjDunXX6VFi0ree0KCmS82Otp7bQMAOIZwCwBwFFMBVcLToyW7eqRkG1vAc1e3ZKtVmjzZDOQ0dKjUv797zuOI0FDpttukCRNMNffMMxkNGQD8DeEWAOAowm0lakvl1t3dkpcvl/77XxMon37at0JkcLDpjgwA8D+EWwCAo+iWXAlPh1t3V27dEW7/+EOaNs2sz5wpnX66688BAKibCLcAAEcRbitRWyq37uyWnJgoHTokdesm3X23648PAKi7CLcAAEcRbivhrdGSXV25tXVLPnzYtfcPb9kiPfecWX/2WdMFGAAAVyHcAgAcRbitRG2p3IaHm/lgJTM9jiucPCndeqtZ/9vffGMQKQBA7UK4BQA4inBbCU+Olnz8uPTnn2bd1ZVbi8X1993Ony99953UrJn06KOuOSYAAKURbgEAjiLcVsKTlVtbl+T69U2l1dVsXZN//bXmx/rtN2nWLLP++OMm4AIA4GqEWwCAowi3lfBGuG3Txj1T6Zx9tnm8917pk0+cP47VKv3f/5lq9oUXSjfe6Jr2AQBQni3c2m6tAQDgVAi3lfBkuLXdb+vqLsk2M2ZIAwZI2dnSkCHSypXOHeeNN6R335Xq1ZMWL/atOW0BALULlVsAgKMIt5WwhduCArO4U+nKrTucdpr0wQfSNdeYwaDGjJEee8xUYqsrJ0e6806zPn26FB3tlqYCACCrVcrNNeuEWwBAdRFuK2GbCkhyf/XW3ZVbSQoNlV57TZoyxTyfPl266y6psLB6r58507SzUydTCQYAwF1OnCj5ApZwCwCoLsJtJUJDS9bdPWKyuyu3NgEB0pNPSk88YZ4/9ZQ0evSpw/tXX0kLF5r1Z54pqWoDAOAOti7JEuEWAFB9hNtKWCwlAbc2VG5LmzbNVHGDg6W1a6XBg6U//rC/b2GhNGmSVFQkJSSYe3YBAHAnW7gNCZECA73bFgCA/yDcVsFTg0p5qnJbWkKCuQ83PFzavFnq10/au7fifs8+K335pRQWZqq+AAC4G4NJAQCcQbitgifCrdXq+cqtzcCB0qZNUtu20k8/SXFx0s6dJT8/cED6xz/M+ty5UqtWnm0fAKBuItwCAJxBuK2CJ8LtkSNm4AzJ8+FWkrp3l7ZuNXPhpqdL/ftLKSnmZ9OmmemDzjtPuvVWz7cNAFA3EW4BAM4g3FbBNmKyO8OtrUty06ZlB7HypLZtpY0bpUGDzNQLl10m3XGHlJRkBqFasoR7ngAAnkO4BQA4g3BbBVvl1p2jJdu6JHvyflt7wsOldeuka6818/o+/bTZftddUs+e3m0bAKBuIdwCAJxBuK2CJ7ol2wZx8kaX5PJCQqTly6V77zXPo6Kk2bO92yYAQN1DuAUAOINwWwVPhNsVK8xjXJz7zuGIgADp0UelL76Qtm2TGjf2dosAAHUN4RYA4IwgbzfAl7k73H7/vbR+vbmfdeJE95zDWeed5+0WAADqKsItAMAZVG6r4O5w++yz5nH4cO/fcwsAgK8g3AIAnEG4rYI7R0vOzZVeecWsT57s+uMDAOCvbOG2USPvtgMA4F8It1VwZ+V2+XIpJ0fq0kW66CLXHx8AAH9F5RYA4AzCbRXcNRWQ1SotWmTWb7tNslhce3wAAPxZbq55JNwCABxBuK2Cuyq3W7dK33xjjn/jja49NgAA/o7KLQDAGYTbKrgr3NqqttddJzVp4tpjAwDg7wi3AABnEG6r4I5we+iQ9PrrZp2BpAAAqIhwCwBwBuG2Cu4YLfmll6T8fDOPbEyM644LAEBtQbgFADiDcFsFV1duCwulJUvMOlVbAADsI9wCAJxBuK2Cq0dL/uADac8ec59tQoJrjgkAQG1DuAUAOINwWwVXV25tA0mNH19ybAAAUBbhFgDgDMJtFVwZbvfulZKTzfqkSTU/HgAAtZHVSrgFADiHcFuFsDDzmJoq/fprzY61ZIm5YA8eLHXuXPO2AQBQG+XnmzEqJMItAMAxhNsq9Ogh9e5t7rkdPlzKyXHuOHl50gsvmHUGkgIAoHK2qq1EuAUAOIZwW4XAQGntWqlVK+n776Ubb5SKihw/zpo1Umam1LatdMUVrm8nAAC1hS3c1qtnFgAAqotwewqtW0tvvCEFB5vHf/7T8WPYBpK65RYpKMi17QMAoDbhflsAgLMIt9XQu3fJ/LSzZpmQW13ffCNt3mxC7cSJbmkeAAC1hi3cNmrk3XYAAPwP4baabrpJuususz5unPTdd9V73eLF5nHkSNO9GQAAVC431zxSuQUAOIpw64DHH5cuushceIcPl/74o+r9s7OlV1816wwkBQDAqdEtGQDgLMKtA4KCpFWrpI4dpd27pYQEqaCg8v1ffdVcpKOjpQsv9Fw7AQDwV4RbAICzCLcOatZMeustc9H96CPp73+3v5/VWtIl+bbbJIvFc20EAMBfEW4BAM5yKtwuWrRIHTt2VGhoqGJiYrRx48Yq91+/fr1iYmIUGhqq008/Xc8++6xTjfUVZ58tvfKKWZ83T1q2rOI+Gzea6YMaNDD36AIAgFMj3AIAnOVwuE1KStKUKVM0Y8YM7dixQ/3799fQoUOVmppqd/89e/bosssuU//+/bVjxw794x//0J133qk1a9bUuPHeNGqU9MADZv2WW6Qvvij7c1vV9vrrpfBwz7YNAAB/RbgFADjL4VlX582bpwkTJmjiX/PazJ8/Xx988IEWL16suXPnVtj/2WefVbt27TR//nxJUnR0tLZt26bHH39co0aNcujcR48eVWBgoKNNdpt775W2b5eSk80AU5s2SS1bSgcPSqtXm33Gjy+5UAMAfM9R/pH2KYRbAICzHAq3+fn52r59u+67774y2+Pj47Vlyxa7r9m6davi4+PLbBsyZIhefPFFnTx5UvXq1avwmry8POXl5RU/z87OliS1bt3akeZ6VHq6dMYZFbf37ev5tgAAardFixbpscceU1pams466yzNnz9f/fv3r3T/9evXa9q0afr+++/VunVrTZ8+XZMmTSqzz5o1a/TAAw/o119/VadOnfTwww9r5MiR7n4rFRBuAQDOcqhbcmZmpgoLCxUZGVlme2RkpNLT0+2+Jj093e7+BQUFyszMtPuauXPnKjw8vHiJiopypJkAANRa7rg9aOvWrUpISNDYsWP19ddfa+zYsRo9erQ+//xzT72tYoRbAICzHO6WLEmWckP/Wq3WCttOtb+97TaJiYmaNm1a8fPs7GxFRUXpwIEDCgsLc6bJbvfRR9LIkWaUZElq2lT63/+k0FDvtgsAULXs7Gyf7hlUnjtuD5o/f74GDx6sxMRESeY6vH79es2fP18rV650qH01vYXoyBHzWK8et/UAAIzq3kLkULiNiIhQYGBghSptRkZGheqsTcuWLe3uHxQUpGbNmtl9TUhIiEJCQipsb9iwoRr66Fe5w4dLjz5q7sOVpAkTzLRBAADfVlhY6O0mVJu7bg/aunWrpk6dWmEfWyC2x923EN1/v1kAAKguh7olBwcHKyYmRikpKWW2p6SkqG8lN5fGxcVV2P/DDz9UbGys3ftt/dndd0t33CGdeaZ0553ebg0AoLZx1+1Ble1T2TElbiECAPgeh7slT5s2TWPHjlVsbKzi4uL03HPPKTU1tXhgisTERO3fv1/L/pr8ddKkSXr66ac1bdo03Xzzzdq6datefPFFh7s5+QOLRVq40NutAADUdu64PcjRY7r7FiKr1VxXAQCo7i1EDofbhIQEHT58WHPmzFFaWpq6d++u5ORktW/fXpKUlpZWZlCLjh07Kjk5WVOnTtUzzzyj1q1ba+HChQ5PAwQAQF3nrtuDKtunsmNK/nkLEQDAP1X3FiKnBpSaPHmyJk+ebPdnS5curbDtwgsv1FdffeXMqQAAwF9K3x5UepqelJQUDR8+3O5r4uLi9M4775TZVv72INstRKXvu/3www8rveUIAABf5FS4BQAA3uGO24PuuusuDRgwQI888oiGDx+ut956Sx999JE2bdrklfcIAIAzCLcAAPgRd9we1LdvX7322mu6//779cADD6hTp05KSkpS7969Pf7+AABwlsVqG1XCh2VnZys8PFxZWVk+O88tAMA/cY1xDT5HAIC7VPca49BUQAAAAAAA+CLCLQAAAADA7xFuAQAAAAB+j3ALAAAAAPB7hFsAAAAAgN8j3AIAAAAA/B7hFgAAAADg9wi3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO8FebsB1WG1WiVJ2dnZXm4JAKC2sV1bbNcaOIdrNQDAXap7rfaLcJuTkyNJioqK8nJLAAC1VU5OjsLDw73dDL/FtRoA4G6nulZbrH7wVXVRUZEOHDigxo0by2KxOH2c7OxsRUVFad++fQoLC3NhC30b75v3XRfwvnnfzrJarcrJyVHr1q0VEMDdOs7iWl0zvG/ed13A++Z9O6u612q/qNwGBASobdu2LjteWFhYnfoPy4b3XbfwvusW3nfNULGtOa7VrsH7rlt433UL77tmqnOt5itqAAAAAIDfI9wCAAAAAPxenQq3ISEhevDBBxUSEuLtpngU75v3XRfwvnnfqB3q6u+W9837rgt437xvd/OLAaUAAAAAAKhKnarcAgAAAABqJ8ItAAAAAMDvEW4BAAAAAH6PcAsAAAAA8HuEWwAAAACA3yPcAgAAAAD8HuEWAAAAAOD3CLcAAAAAAL9HuAUAAAAA+D3CLQAAAADA7xFuAQAAAAB+j3ALAAAAAPB7hFsAAAAAgN8j3AIAAAAA/B7hFgAAAADg9wi3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO8RbgEAAAAAfo9wCwAAAADwe4RbAAAAAIDfI9wCAAAAAPwe4RYAAAAA4PcItwAAAAAAv0e4BQAAAAD4PcItAAAAAMDvEW4BAAAAAH6PcAsAAAAA8HuEWwAAAACA3yPcAgAAAAD8HuEWAAAAAOD3CLcAAAAAAL9HuAUAAAAA+D3CLQAAAADA7xFuAQAAAAB+j3ALAAAAAPB7hFsAAAAAgN8j3AIAAAAA/B7hFgAAAADg9wi3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO8RbgEAAAAAfo9wCwAAAADwe4RbAAAAAIDfI9wCAAAAAPwe4RYAAAAA4PcItwAAAAAAv0e4BQAAAAD4PcItAAAAAMDvEW4BAAAAAH6PcAsAAAAA8HuEWwAAAACA3yPcAh60dOlSWSyW4iU0NFQtW7bUoEGDNHfuXGVkZHi7icXKtzUoKEitWrXStddeq127dnm7eQAAeJS3r4vePj/gD4K83QCgLnr55ZfVtWtXnTx5UhkZGdq0aZMeeeQRPf7440pKStIll1zi7SYWs7X1xIkT2rx5sx5++GF98skn+umnn9SkSRNvNw8AAI/y9nXR2+cHfBnhFvCC7t27KzY2tvj5qFGjNHXqVF1wwQW66qqrtGvXLkVGRnqxhSVKt3XgwIEqLCzUgw8+qDfffFPjx4/3cusAAPAsb18XvX1+wJfRLRnwEe3atdMTTzyhnJwcLVmyRJI0a9YsWSwW7dixQ1dddZXCwsIUHh6uG264QYcOHXL6XE8++aTefPNNp15ru6AePHjQ6fMDAFBb1PS6WJNrsivOD9QmhFvAh1x22WUKDAzUhg0bymwfOXKkzjjjDK1evVqzZs3Sm2++qSFDhujkyZNOnWfbtm0aPXq0UxfTPXv2SJK6dOni1LkBAKhNanpdrMk12RXnB2oTuiUDPqRhw4aKiIjQgQMHymy/6qqr9Oijj0qS4uPjFRkZqeuvv16rVq3S9ddf7/B5li1bJkkaPXq0Vq1apREjRlS6b2FhoQoKCorv7XnooYc0YMAAXXnllQ6fFwAAf+fq66Ij12R3nB+oTQi3gI+xWq0VtpUPsKNHj9aNN96oTz75pMLPMjMz1bx582qfb/To0dq3b1+l9/j26dOnzPPo6Gi99dZbCgrinw8AQN3jyHXR1ddkR88P1DX8XwD4kKNHj+rw4cM6++yzy2xv2bJlmedBQUFq1qyZDh8+XOEYjRs31vPPP3/Kc73//vtas2aNhg8frmbNmlW637JlyxQdHa2cnBwlJSVpyZIluu6667Ru3bpqvisAAGoPR66Lrr4mO3p+oK4h3AI+5L333lNhYaEGDhxYZnt6erratGlT/LygoECHDx+2ewEMCQnRxIkTT3med999V1dffbVWrlxZ5be90dHRxYNVDBo0SIWFhXrhhRe0evVqXX311Q68OwAA/J8j10VXX5MdPT9Q1zCgFOAjUlNTdc899yg8PFy33nprmZ8tX768zPNVq1apoKCgQgiurscee0zDhg2r1kW0vEcffVRNmjTRzJkzVVRU5NT5AQCoLWp6XazJNdkV5wdqE8It4AXfffedPvvsM23atElr167V1KlTdc455+jo0aN64403Ktyfs3btWk2fPl0pKSmaP3++br31VvXo0UOjR4926vzvvPOO0xfRJk2aKDExUT/++KNWrFjh1PkBAKgtanpdrMk12RXnB2oTwi3gBePHj1dcXJwuvvhi3XbbbdqxY4f+/ve/66efftKgQYMq7L927Vr99NNPuuqqqzRz5kwNGzZMH374oYKDg506f+PGjWs08MQdd9yhdu3aac6cOSosLHT6OAAA1AY1uS7W9Jpc0/MDtYnFam9oVgA+YdasWZo9e7YOHTqkiIgIbzcHAAAA8FlUbgEAAAAAfo9wCwAAAADwe3RLBgAAAAD4PSq3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO/VbFItDykqKtKBAwfUuHFjWSwWbzcHAFCLWK1W5eTkqHXr1goI4DtfZ3GtBgC4S3Wv1X4Rbg8cOKCoqChvNwMAUIvt27dPbdu29XYz/BbXagCAu53qWu0X4bZx48aSzJsJCwvzcmsAALVJdna2oqKiiq81cA7XagCAu1T3Wu0X4dbWvSksLIwLJgDALehKWzNcqwEA7naqazU3FwEAAAAA/B7hFgAAAADg9wi3AAAAAAC/R7gFAAAAAPg9wi0AAAAAwO8RbgEAAAAAfs+pcLto0SJ17NhRoaGhiomJ0caNGyvd96abbpLFYqmwnHXWWU43GgAAAACA0hwOt0lJSZoyZYpmzJihHTt2qH///ho6dKhSU1Pt7r9gwQKlpaUVL/v27VPTpk11zTXX1LjxAAAAAABIToTbefPmacKECZo4caKio6M1f/58RUVFafHixXb3Dw8PV8uWLYuXbdu26c8//9T48eNr3HgAAAAAACQHw21+fr62b9+u+Pj4Mtvj4+O1ZcuWah3jxRdf1CWXXKL27dtXuk9eXp6ys7PLLAAAAAAAVMahcJuZmanCwkJFRkaW2R4ZGan09PRTvj4tLU3r1q3TxIkTq9xv7ty5Cg8PL16ioqIcaSYAAAAAoI5xakApi8VS5rnVaq2wzZ6lS5fqtNNO04gRI6rcLzExUVlZWcXLvn37nGkmAAAAAKCOCHJk54iICAUGBlao0mZkZFSo5pZntVr10ksvaezYsQoODq5y35CQEIWEhDjSNAAAAABAHeZQuA0ODlZMTIxSUlI0cuTI4u0pKSkaPnx4la9dv369fvnlF02YMMG5lgI+aPt26YsvzHpAgGSx2H8svR4UJPXoIXXubLYBAGoPq1VKTTXXhk6dpF69vN0iAKg7HAq3kjRt2jSNHTtWsbGxiouL03PPPafU1FRNmjRJkulSvH//fi1btqzM61588UX17t1b3bt3d03LAS/Jz5fWrJEWLpQ++8z547RsKQ0cKF14oXk880zCLgD4m2PHpG3bzPVg61bzaOvgFhwspaRIAwZ4t40AUFc4HG4TEhJ0+PBhzZkzR2lpaerevbuSk5OLRz9OS0urMOdtVlaW1qxZowULFrim1YAXHDwoLVkiLV5c8odLvXrSJZdI9etLRUXmG/tTPR49Ku3YYY7x2mtmkaTISBN0bWE3OtozYffYMenwYSkwUAoLkxo2JGQDgD1Wq/Trr2WD7NdfS4WFZfcLCjL/pu/fLw0fLm3ZYv5NBwC4l8VqtVq93YhTyc7OVnh4uLKyshQWFubt5qCO+fJL6amnpKQkU7WVTNX1ttukW24x6446cUL6/HPp00+l9evNH0knTpTdp3nzkrDbtasJn7YuzqdaCgulP/6QMjNPvRw/Xva8FovUuLEJuuUfS683bSq1aye1b2+W5s0JxfBPXGNcozZ/jnv2SNOmSZs2mX83y2vdWoqLM0ufPiVdkS++2Pz73qGDeXTmegEAqP41hnAL2FFZ1+M+faQ775RGjTLdzVwlL8/cn2ULu1u2VAyd7lSvnqksl68+OKJ+/bJht/zSurWpZrhDQYH5nRUUmHPUq2ceCduoDq4xrlGbP8dbbpGef96sBwdLMTHmemALs5XNWJiZafb55Rfzmk8/lRo18lizAaDWINwCTqis6/G110p33CGdd55n2pGfbyrGtrCbllbStbk6i8UiNWsmRURUb2nY0Jz3+HEpO1vKyTGPVa0fOiT99ptZ0tJO/Z4CAqSQkJLgWa9e2XV726xW81nk5ZV9LL9eVGT/nKWPaW8JDjahvEED8xmUf7S3LSTEfL62RbK/Xvp5YaFZCgqq92i12j9eZUtAgKns21uCguxvtx2/9BWg/NWg/M9K/zdWneeOquxzrGo55xypbVvHz1Ua1xjXqK2fY0GB1KqVCaovvyxdd535d6C6fvnFBNzMTOnyy6U333TfF30AUFsRboFqOnZMeustafly6YMPzB8yUs27HtcleXnSvn0m6KamloRe27Jvn3TypLdbidroxRelv/2tZsfgGuMatfVz/OQT6aKLzK0Y6enmizFHffaZOcbx49KkSdKiRfQsAQBHVPcaw3eH8BtZWeY+0g4dav5HQUGB9PHHJtC+8YaUm1vys969pbvucn3X49osJEQ64wyz2FNYaCq9J06YkHvypPkdnGo9IMD8DkJCzGPpdXuPgYFlX1/Zkp9fsn78uBnk69ixso+VrefllVQlrdbK10tvK11BPdWj7d5q22urs9i6lFe22CrCpZfSSv//VP7/rdLPbRXf8tNblV+3PXdU+c+tOkuzZo6fB3DE6tXmccQI54KtZLour1ghXXWV9Oyz5laN++5zWRMBAH8h3MLnFRRICxZIM2eagNGkiRQbW3aJijr1H9NWq5muYflyM0LxwYMlP+vYURozRrr+eka0dIfAQKrfAPxPUZH5AlQyX3jWxIgR5lp2551SYqIZo2DMmBo3EQBQCuEWPm3HDunmm6Xt283zgADpzz/NvIEpKSX7tWhRNuyed15JmPrlFxNoly+Xdu0qeU2zZlJCggm0cXF0EQMAlLV1qxlTICzMjHxcU3fcIe3dK82bJ40fL7VpY0bEBwC4BuEWPunYMenBB6UnnzRdKE87TXr8cemGG6TvvzcV2G3bzKBL330nZWRIyclmsWnTxgyW9PXXJdvq1zdzDt5wgxQf73wXMwBA7WfrknzllY4NIlWVxx4zYxOsXm2quZs3S926uebYAFDXEW7hc1JSpFtvNfMKStLo0aYrl60S26uXWW65xTw/flz65puSsLttm/Tjj9L+/WYJCJAGDzYV2hEjzDytAABUxWo1U8JJNe+SXFpAgPTqq6YivHmzNHSoGXCqVSvXnQMA6irCLXxGZqY0bZq56EvmPtpFi6Qrrqj6dfXrm0Ggevcu2ZabK+3cKf3+uzRokBQZ6bZmAwBqoS+/NCO9N2woDRni2mOHhppR+uPizO0yl18ubdjAHLgAUFMB3m4AYLVK//mPGcjp1VfNva933mm6H58q2FamUSPpggvM/LQEWwCAo2xV28svN1+iulqzZtK6dVLz5mZ8idGjS6aiAwA4h3ALr9qzx3TJGjvWVG7PPtsM4LFgAd2HAQDe4a4uyeV16iS9+64Jz+vWmbnVjx1z3/kAoLYj3MIrioqkJ56QuneXPvjADNTx8MNmVOTS3YsBAPC0r7+Wfv3VdB++7DL3nuv88830dAEB0gsvSOHhprvy3/8uvfeedOSIe88PALUJ4RZesWiRdM895hvqQYOkb7+V/vEPRi8GAHifrWp76aWeuQ/2yiulpUultm1N1+TPPpMefdTcmtO0qXTuueZ2nddfl9LT3d8eAPBXhFt4XH6+uWhL0qxZ0scfS507e7VJAAAU80SX5PLGjjVTBO3eLb3yijRhgrk2Wq2mkvzUU+a+3FatpDPPlCZOlJYto7ILAKVZrFar1duNOJXs7GyFh4crKytLYWFh3m4OauiVV6SbbjJT++zZY7p9AYC3cI1xjdryOf7wg3TWWaYn0aFDppuwN6WnSxs3mtGUN240U9+V/sutRQtp4UITfC0W77UTANyputcYKrfwqKKikqrt1KkEWwBwtQ0bNmjYsGFq3bq1LBaL3nzzzSr3X7t2rQYPHqzmzZsrLCxMcXFx+uCDDzzTWB9kq9oOHuz9YCuZL4KvucZUbnfulA4flt55R5o+3VR2MzLMzADDh5vp7wCgLiPcwqPefdd8Kx4WJt16q7dbAwC1z9GjR9WjRw89/fTT1dp/w4YNGjx4sJKTk7V9+3YNGjRIw4YN044dO9zcUt/kjS7JjmjSxNyL+8gjZryKWbNMlfmdd6Ru3aTFi80XyQBQF9EtGR5jtUr9+pmpfu67T5o719stAoDafY2xWCx64403NGLECIded9ZZZykhIUEzZ86s9mtqw+f4yy+mGhoYKB08aOai9Qc//GDuwd261Ty/4ALp+eelrl292y4AcBW6JcPnbNpkLrwhIdJdd3m7NQAAe4qKipSTk6OmTZtWuV9eXp6ys7PLLP7OVrUdNMh/gq1kKrYbN5p7bxs2NNfbHj2khx4ygzgCQF1BuIXH/Pvf5tE2mBQAwPc88cQTOnr0qEaPHl3lfnPnzlV4eHjxEhUV5aEWus/q1ebRV7skVyUwULrjDlPFHTrUhNoHHpBiY6Uvv/R26wDAMwi38IhvvpGSk80k9ffc4+3WAADsWblypWbNmqWkpCS1aNGiyn0TExOVlZVVvOzbt89DrXSP336Ttm0zIw6PHOnt1jivXTvpvfek5culiAhzX26fPtK0adLRo95uHQC4F+EWHmEbIfnqq6UzzvBuWwAAFSUlJWnChAlatWqVLrnkklPuHxISorCwsDKLP1u71jz27y9FRnq3LTVlsUhjxkg//ijdcIMZYOrJJ6Xu3aWUFG+3DgDch3ALt9u7V3rtNbP+9797tSkAADtWrlypm266SStWrNDll1/u7eZ4ha1L8tVXe7cdrhQRIb36quk51a6duR7Hx5sRlhlRGUBtRLiF2z3xhFRYaOYM7NXL260BgNotNzdXO3fu1M6dOyVJe/bs0c6dO5WamirJdCceN25c8f4rV67UuHHj9MQTT6hPnz5KT09Xenq6srKyvNF8rzhwQNqyxaxfdZV32+IOQ4dK338v3XabeT57tul6XQvGAAOAMgi3cKtDh6QXXzTr993n3bYAQF2wbds29ezZUz179pQkTZs2TT179iye1ictLa046ErSkiVLVFBQoNtvv12tWrUqXu6qQ8Pav/GGeezTR2rTxrttcZdGjaRFi6RXXjGzFrz9ttS7t/Tzz95uGQC4TpC3G4Da7amnpOPHzWiNgwZ5uzUAUPsNHDhQVU1hv3Tp0jLPP/30U/c2yM0KC6UFC8w86r17O3eM2tgluTLjxpmpg0aOlH76STr/fOk//5GGDfN2ywCg5qjcwm1yc6Wnnzbr991nBrgAAMCV3nxTuvtuacAA6Z13HH99Roa0YYNZr41dku2JjTUjQ/fvb7omX3ml9M9/ch8uAP9HuIXbPP+89OefUufO0ogR3m4NAKA2+vhj85ifb8Lp66879vo33zShrlcvqWNHlzfPZ0VGSh99JN1+u3k+c6aZ3zcnx7vtAoCaINzCLfLzpXnzzPr06WZyeQAAXO2TT8xj9+5SQYF07bWmm211rVljHutCl+TygoNND6sXXzTrb75p7jvetcvbLQMA5xBu4RYrVki//y61aiWNHevt1gAAaqO0NHPfqMViQu7f/maqsOPGmd5Dp/LHH9J//2vWR41yb1t92d/+Zrpmt24t/fCDdN55ZvogAPA3hFu4XFGR9MgjZn3qVDMqIwAArmYbC+vcc82crs8/L02eLFmt0i23mEENq/L226ba27271KWLu1vr23r3lrZvl/r2lbKypCuukP71L/NZAoC/INzC5d55x3yTHh4u3Xqrt1sDAKitbOHWNhp/QIDpZnv33eb5nXdKjz5a+evrcpdke1q2NBXwSZNMqJ0xQ7rmGikz09stA4DqIdzCpaxW6d//NuuTJ0thYd5tDwCg9rLdb1t6qjmLRXrsMemBB8zzv/9dmj27YgUyO1v68EOzXpe7JJcXHCwtXiwtWSLVq2e+AGjb1txitHkzlVwAvo1wC5fauFH67DPTFfmuu7zdGgBAbbV/vxn4KCDATGlTmsUizZkjPfyweT5rlpSYWDaYvfuuGfzwzDOls87yWLP9xi23mMp4r15SXp4ZpOuCC6QePaRFi8yXAwDgawi3cClb1Xb8eDPNAAAA7mCr2vbqZW6Dsecf/5CefNKsP/KINGVKScC1dUkeNYp52CvTt6+5D/eLL8ygU/XrS99+a6YPat3aBOCvvvJ2KwGgBOEWLvPNN9K6deZb9Hvu8XZrAAC1Wfn7bSszZYr07LNmfeFCMxZETo65Xkl0Sa6O884z0wUdOGA+w27dpKNHzQBeMTHS+edLL78sHTvm7ZYCqOsIt3AZ2wjJ11wjderk3bYAAGo3e/fbVubWW6WlS82Xr88/b7rXHj8udewo9ezp1mbWKqedJt1xh/Tdd9L69dJ115n7cr/80lR2W7c2tyR98w335gLwDsItXGLPHikpyaz//e/ebQsAoHZLTZV275YCA01QrY4bbzRzsAcGmvAl0SXZWRaLNGBAyZz2jzwinX66mUJo4UJzX27Xrma05R07CLoAPIdwC5d44gmpsFCKj+dbcACAe9mqtrGxUuPG1X9dQoK51zY42DwfPdr1batrWrSQpk83g3u9/740cqQZVPJ//zPz5PbqJXXuLN13n7RtG0EXgHsRblFjaWnmXhyJqi0AwP2qe7+tPcOHS1u3Sm+8Ye4lhWsEBEhDhkhr10qHDpmq7lVXSaGh0q+/murueeeZruD33GNmVigq8narAdQ2hFvU2OOPSydOmFEVnflDAwAARzhyv609vXpJI0a4rDkop3Fjcz/umjUm6K5aZarkDRpIv/1menvFxUnt25sBvzZtIugCcA2nwu2iRYvUsWNHhYaGKiYmRhs3bqxy/7y8PM2YMUPt27dXSEiIOnXqpJdeesmpBsO3ZGSUjEL5wAPcuwQAcK89e0xACgoyX6rCtzVqZAaaTEoyQXftWmnMGBOAf/9dWrDAzFPcqZN0//3Szz97u8UA/JnD4TYpKUlTpkzRjBkztGPHDvXv319Dhw5Vampqpa8ZPXq0Pv74Y7344ov6+eeftXLlSnXt2rVGDYdvmDfPDP0fG2u6IwEA4E62qu3555vgBP/RoIG5J3f5cvPl+NtvS2PHmqC7d6/08MNmIKrzzjMDU2VkeLvFAPyNxWp17Nb+3r17q1evXlq8eHHxtujoaI0YMUJz586tsP/777+va6+9Vrt371bTpk2damR2drbCw8OVlZWlsLAwp44B1zt8WOrQQcrNNReoYcO83SIAcBzXGNfw1Oc4bpz06qtmJN6HHnLbaeBBx4+bvyP+8x8zKFVBgdkeGGi+OB87VrryShOOAdRN1b3GOFS5zc/P1/bt2xUfH19me3x8vLZs2WL3NW+//bZiY2P16KOPqk2bNurSpYvuueceHT9+vNLz5OXlKTs7u8wC37NggQm2554rXXGFt1sDAKjtrNaa328L31O/vhnJ+p13pAMHpKeeMpX5wkIpOdncv9uypTR+vPTxx2Y7ANjjULjNzMxUYWGhIiMjy2yPjIxUenq63dfs3r1bmzZt0nfffac33nhD8+fP1+rVq3X77bdXep65c+cqPDy8eImKinKkmfCAI0dMuJXMPTLcawsAcLdffzX3adarZwYkQu3TvLn0f/8nff65uf/2gQfMCMs5OdLSpdIll5iBqCZMkJYsMfPonjzp7VYD8BVODShlKZdkrFZrhW02RUVFslgsWr58uc4//3xddtllmjdvnpYuXVpp9TYxMVFZWVnFy759+5xpJtzoqaek7GzprLPM/TMAALibrWrbpw9dVOuCLl2kOXPMlxqbNkm33io1aSLt3y+99JI0aZIZ+TosTOrXT5o6VVq50uzPfLpA3RTkyM4REREKDAysUKXNyMioUM21adWqldq0aaPw8PDibdHR0bJarfr999/VuXPnCq8JCQlRSEiII02DB+XkSE8+adbvv9/MbQcAgLvVZH5b+C+LxYTXfv1Mr7GPPpK2bJG++EL68kspK8s8L32HXNOmpmuzbTnnHNO1uV49770PAO7nULgNDg5WTEyMUlJSNLJUuS4lJUXDhw+3+5p+/frp9ddfV25urhr9Nazh//73PwUEBKht27Y1aDq85ZlnpD//lM480wzvDwCAu3G/LSQpJES6/HKzSGZ+3F27TMj94guz7Ngh/fGHGZzq/fdLXmuxSC1aSK1bV700b24GswLgfxweLTkpKUljx47Vs88+q7i4OD333HN6/vnn9f3336t9+/ZKTEzU/v37tWzZMklSbm6uoqOj1adPH82ePVuZmZmaOHGiLrzwQj3//PPVOicjWfqOo0fNCMmZmdKyZWYEQwDwZ1xjXMPdn+PPP5tpYkJCzLgPoaEuPwVqifx86ZtvSsLu559Lv/xSMgrzqQQGSpGRJgg3b26WiIiS9dJLRISpEtOLDXCv6l5jHKrcSlJCQoIOHz6sOXPmKC0tTd27d1dycrLat28vSUpLSysz522jRo2UkpKiO+64Q7GxsWrWrJlGjx6thxi/3y8tWWKCbadOZvRCAAA8wVa1jYsj2KJqwcFSbKxZJk8224qKzN8vBw5UvRw8aEZjtj2vjoAAqVkzs5x2mrkvuEmTkvXyj7b18HDzZU1wsBQUxOCcrmS1ltx37ci6veeVbXPk51Wdt/xj+W2Ovu+abHf0eXW1b2/ms/YEhyu33sC36r7h+HEzYuHBg9ILL5iRCgHA33GNcQ13f47XXislJUmzZ0szZ7r88IAkU93NyDDB9tAhE4gPHap8ycpyzXktFhNyQ0JKAq+9x9IV4tJhuLJ1m9J/7dsLT5Wlgeqcw16IKyqq+nlhodn2/+3df3RU9Z3/8deQkAmCGcuvkEAIUSumRhGTFRNEFEpstB497X5N5RSwhZasqA3Rbo2cb1WO29geZWNXE2URLLtIsxVs3W/jj3QVCEb7lTRZ+QptUX4kxYlp4poJaCeS3O8f14kM+cFMMsmdO/f5OOeeubm5d+Z9cyUfX/l87v0EXk9f729bYAm8V39fn/45iC7/5/98cSvBUI1Yzy2ca9MmM9impzMcGQAwegyDh0lhdMTHf3HvbSi6uqT2djPofvSR+UySjz/u//XMbadPGmIYkt9vLrAvl+vsy+n7BdbPfD1zW7g1DGd7uF+HIiEh/GOGinCLkPj90k9/aq7fd9/o/kcKAHC2gwfNP64mJppPvgWiRUKClJJiLuH67DMzHHd1mf+fNdDr6esDDWcdbFt/QWmwbacLZZjqmeFtzJjBv3a5zPuax4zp+9rfemD/wHsF3i+wfubXZwuUA4XIoex75n6wHuEWIdmyxZxXbvp06TvfsboaAICTBO63nT/fHJoJxIKxY81l/HirKwFiB892w1l99pn0yCPm+o9+xP9YAABGF0OSAQChINzirP7t36Rjx8zH4q9aZXU1AAAn6ekh3AIAQkO4xaBOnZL+6Z/M9X/8R2ncOGvrAQA4y7vvmk+sPeccc2oXAAAGQrjFoLZvlw4fNicpX73a6moAAE4TuN/26qt5mCEAYHCEWwyou/uLXtt77uGBBwCA0ceQZABAqAi3GNCvfiX96U/SxInSmjVWVwMAcJqeHmn3bnOdcAsAOBvCLfrV0yM9/LC5XlwsnXuupeUAABzonXekjz6SJkyQrrjC6moAANGOcIt+vfCC+RCPpCTprrusrgYA4ESB+20XLDDnAwUAYDCEW/TR3CytX2+u/+AH0nnnWVoOAMChAuGWIckAgFDEW10ArGcY0v790m9+Yy719eb2CRPMIckAAIy27m5pzx5znXALAAgF4dahTp2S9u6Vfv1rM9AePfrF91wuKTdXevBB82FSAACMtsZGqaPDvD3m8sutrgYAYAeEWwc5cUJ65RUzzP72t+ZDOgISE6UlS6Sbb5a+/nUpOdm6OgEACAxJvuYaKZ7/WwEAhIDmwgEOHJD+8R+l3/1O8vu/2D5pkhlkb7nFDLbMYwsAiBbcbwsACBfh1gHWrTN7aiXpggvM3tmbb5by8vhrOAAg+pw6JdXWmuuEWwBAqIg2Mc7vN3tsJemll6TrrzfvqQUAIFr94Q9SZ6f5tP7LLrO6GgCAXTAVUIzbu9e813baNCk/n2ALAIh+gSHJCxdKcXHW1gIAsA/CbYx76SXztaBAGsPVBgDYAPfbAgCGgrgT46qrzdeCAmvrAACMjj179uimm25SamqqXC6Xfv3rX5/1mN27dys7O1uJiYk6//zz9dRTT418oQP47DNz1JFEuAUAhIdwG8OOHpUOHjSHdC1ZYnU1AIDRcPLkSc2ZM0dPPPFESPsfOXJEN9xwgxYsWKCGhgbdf//9uvvuu7Vjx44RrrR/+/ZJJ0+aT/TPyrKkBACATfFAqRgWGJI8f775UA4AQOwrKChQQRjDdZ566inNnDlT5eXlkqTMzEzt27dPjz76qL75zW8OeJzf75f/tPnlfD7fkGs+3en323I7DQAgHDQbMYwhyQCAs3nzzTeVn58ftO3666/Xvn379Nlnnw14XFlZmTweT++SlpYWkXq43xYAMFSE2xj1t79J//Vf5voNN1hbCwAgerW0tCg5OTloW3Jysk6dOqW2trYBjystLVVHR0fv0tzcHJF6ioqk732P22kAAOFjWHKM2r1b+vRTafp06dJLra4GABDNXGfME2cYRr/bT+d2u+V2uyNeyze/aS4AAISLntsYdfoUQMxtCwAYyLRp09TS0hK0rbW1VfHx8Zo0aZJFVQEAED7CbYwK3G/LkGQAwGByc3NVU1MTtO3VV19VTk6Oxo4da1FVAACEj3Abg957Tzp0SBo7Vlq82OpqAACj6cSJE2psbFRjY6Mkc6qfxsZGNTU1STLvlV2+fHnv/kVFRTp27JhKSkp08OBBbd68Wc8884zuvfdeK8oHAGDIuOc2BgWGJF99tZSUZG0tAIDRtW/fPl132qOGS0pKJEkrVqzQs88+K6/X2xt0JSkjI0PV1dVau3atnnzySaWmpurnP//5oNMAAQAQjQi3MYghyQDgXNdee23vA6H68+yzz/bZtnDhQv3hD38YwaoAABh5DEuOMZ988sUcgYRbAAAAAE5BuI0xu3ZJfr80c6aUmWl1NQAAAAAwOgi3Meb0IclMAQQAAADAKQi3McQwuN8WAAAAgDMRbmPIn/8sHTkiJSRIixZZXQ0AAAAAjB7CbQwJ9NouXCiNH29tLQAAAAAwmgi3MYQhyQAAAACcinAbI06ckPbsMdcLCqytBQAAAABGG+E2Rrz2mtTVJZ1/vnTRRVZXAwAAAACji3AbI5gCCAAAAICTEW5jwOlTADEkGQAAAIATDSncVlRUKCMjQ4mJicrOzlZtbe2A++7atUsul6vP8sc//nHIRSPYgQNSc7OUmChde63V1QAAAADA6As73FZVVam4uFjr1q1TQ0ODFixYoIKCAjU1NQ163J/+9Cd5vd7e5ctf/vKQi0awQK/tdddJ55xjbS0AAAAAYIWww+2GDRu0cuVKrVq1SpmZmSovL1daWpoqKysHPW7q1KmaNm1a7xIXFzfkohGMIckAAAAAnC6scNvV1aX6+nrl5+cHbc/Pz1ddXd2gx86dO1cpKSlavHixXn/99UH39fv98vl8QQv65/NJe/ea64RbAAAAAE4VVrhta2tTd3e3kpOTg7YnJyerpaWl32NSUlK0ceNG7dixQzt37tTs2bO1ePFi7QlMytqPsrIyeTye3iUtLS2cMh3ld7+TTp0yp/+58EKrqwEAAAAAa8QP5SDXGXPNGIbRZ1vA7NmzNXv27N6vc3Nz1dzcrEcffVTXXHNNv8eUlpaqpKSk92ufz0fAHQBDkgEAAAAgzJ7byZMnKy4urk8vbWtra5/e3MFcddVVOnTo0IDfd7vdSkpKClrQl2FIL71krt9wg7W1AAAAAICVwgq3CQkJys7OVk1NTdD2mpoa5eXlhfw+DQ0NSklJCeej0Y933pE++MB8QvIAneAAAAAA4AhhD0suKSnRsmXLlJOTo9zcXG3cuFFNTU0qKiqSZA4pPn78uLZu3SpJKi8v16xZs3TJJZeoq6tL//7v/64dO3Zox44dkT0TBwoMSV60yJzjFgAAAACcKuxwW1hYqPb2dq1fv15er1dZWVmqrq5Wenq6JMnr9QbNedvV1aV7771Xx48f17hx43TJJZfot7/9rW5gHO2wBcItP0oAAAAATucyDMOwuoiz8fl88ng86ujo4P7bz/3P/0hTpkjd3dKRI9KsWVZXBAD2RBsTGfwcAQAjJdQ2Jqx7bhE9amrMYJuZSbAFAAAAAMKtTTEkGQAAAAC+QLi1oZ4e6eWXzXXCLQAAAAAQbm2poUH68ENpwgTp6qutrgYAAAAArEe4taHAkOSvflVKSLC2FgAAAACIBoRbG3rpJfOVIckAAAAAYCLc2synn0q//725fv311tYCAAAAANGCcGsz771nPlDqvPOktDSrqwEAAACA6EC4tZlDh8zXiy6SXC5rawEAAACAaEG4tZk//9l8/fKXra0DAAAAAKIJ4dZmTu+5BQAAAACYCLc2Q88tAAAAAPRFuLUZem4BAAAAoC/CrY34fNKHH5rr9NwCAAAAwBcItzYS6LVNTpaSkqytBQAAAACiCeHWRgLhll5bAAAAAAhGuLURHiYFAAAAAP0j3NoID5MCAAAAgP4Rbm2EnlsAAAAA6B/h1iYM44twS88tAAAAAAQj3NpEe7v08cfm+gUXWFoKAAAAAEQdwq1NBO63TUuTzjnH2loAAAAAINoQbm2C+20BAAAAYGCEW5vgSckAgHBUVFQoIyNDiYmJys7OVm1t7aD7b9u2TXPmzNE555yjlJQUfec731F7e/soVQsAwPARbm2CnlsAQKiqqqpUXFysdevWqaGhQQsWLFBBQYGampr63X/v3r1avny5Vq5cqXfffVe/+tWv9Pbbb2vVqlWjXDkAAENHuLUJem4BAKHasGGDVq5cqVWrVikzM1Pl5eVKS0tTZWVlv/u/9dZbmjVrlu6++25lZGTo6quv1urVq7Vv375RrhwAgKEj3NqAYXwRbum5BQAMpqurS/X19crPzw/anp+fr7q6un6PycvL01/+8hdVV1fLMAx9+OGHev7553XjjTcO+Dl+v18+ny9oAQDASoRbG/B6pZMnpbg4KSPD6moAANGsra1N3d3dSk5ODtqenJyslpaWfo/Jy8vTtm3bVFhYqISEBE2bNk3nnXee/uVf/mXAzykrK5PH4+ld0tLSInoeAACEi3BrA4H7bWfNkhISLC0FAGATLpcr6GvDMPpsCzhw4IDuvvtu/fjHP1Z9fb1efvllHTlyREVFRQO+f2lpqTo6OnqX5ubmiNYPAEC44q0uAGfH/bYAgFBNnjxZcXFxfXppW1tb+/TmBpSVlWn+/Pn64Q9/KEm67LLLNH78eC1YsEAPP/ywUlJS+hzjdrvldrsjfwIAAAwRPbc2wJOSAQChSkhIUHZ2tmpqaoK219TUKC8vr99jPvnkE40ZE/y/BHFxcZLMHl8AAOyAcGsD9NwCAMJRUlKiTZs2afPmzTp48KDWrl2rpqam3mHGpaWlWr58ee/+N910k3bu3KnKykodPnxYb7zxhu6++25deeWVSk1Nteo0AAAIC8OSbYCeWwBAOAoLC9Xe3q7169fL6/UqKytL1dXVSk9PlyR5vd6gOW9vv/12dXZ26oknntA999yj8847T4sWLdJPf/pTq04BAICwuQwbjDfy+XzyeDzq6OhQUlKS1eWMqu5u6ZxzpK4u6cgR86FSAIDIcXIbE0n8HAEAIyXUNoZhyVGuqckMtgkJErMsAAAAAED/CLdRLnC/7YUXmvPcAgAAAAD6ItxGOe63BQAAAICzI9xGOZ6UDAAAAABnR7iNcvTcAgAAAMDZEW6jHD23AAAAAHB2hNsoFpj+R6LnFgAAAAAGM6RwW1FRoYyMDCUmJio7O1u1tbUhHffGG28oPj5el19++VA+1nGOHJF6eqTx46WUFKurAQAAAIDoFXa4raqqUnFxsdatW6eGhgYtWLBABQUFampqGvS4jo4OLV++XIsXLx5ysU5z+v22Lpe1tQAAAABANAs73G7YsEErV67UqlWrlJmZqfLycqWlpamysnLQ41avXq2lS5cqNzd3yMU6DffbAgAAAEBowgq3XV1dqq+vV35+ftD2/Px81dXVDXjcli1b9P777+uBBx4I6XP8fr98Pl/Q4kQ8KRkAAAAAQhNWuG1ra1N3d7eSk5ODticnJ6ulpaXfYw4dOqT77rtP27ZtU3x8fEifU1ZWJo/H07ukpaWFU2bMoOcWAAAAAEIzpAdKuc64AdQwjD7bJKm7u1tLly7VQw89pIvCSGilpaXq6OjoXZqbm4dSpu3RcwsAAAAAoQmtK/VzkydPVlxcXJ9e2tbW1j69uZLU2dmpffv2qaGhQXfeeackqaenR4ZhKD4+Xq+++qoWLVrU5zi32y232x1OaTHnk0+kv/zFXKfnFgAAAAAGF1bPbUJCgrKzs1VTUxO0vaamRnl5eX32T0pK0v79+9XY2Ni7FBUVafbs2WpsbNS8efOGV30Me+898/VLX5ImTbK2FgAAAACIdmH13EpSSUmJli1bppycHOXm5mrjxo1qampSUVGRJHNI8fHjx7V161aNGTNGWVlZQcdPnTpViYmJfbYjGPfbAgAAAEDowg63hYWFam9v1/r16+X1epWVlaXq6mqlp6dLkrxe71nnvMXZcb8tAAAAAITOZRiGYXURZ+Pz+eTxeNTR0aGkpCSryxkV3/2utGWLtH699L//t9XVAEDscmIbMxL4OQIARkqobcyQnpaMkUfPLQAAAACEjnAbpbjnFgAAAABCR7iNQh0dUmuruU7PLQAAAACcHeE2CgV6bZOTpXPPtbYWAAAAALADwm0UCtxvy5BkAAAAAAgN4TYKBXpuGZIMAAAAAKEh3EYhem4BAAAAIDyE2yhEzy0AAAAAhIdwG2UMg55bAAAAAAgX4TbKtLWZUwFJ0gUXWFsLAAAAANgF4TbKBHptZ86Uxo2zthYAAAAAsAvCbZThflsAAAAACB/hNspwvy0AAAAAhI9wG2XouQUAAACA8BFuoww9twAAAAAQPsJtFOnpkd57z1yn5xYAAAAAQke4jSIffCB98okUFydlZFhdDQAAAADYB+E2igTut83IkMaOtbYWAAAAALATwm0U4X5bAAAAABgawm0U4UnJAAAAADA0hNsoQs8tAAAAAAwN4TaK0HMLAAAAAENDuI0S3d3S+++b6/TcAgAAAEB4CLdR4tgx6bPPJLdbSkuzuhoAAAAAsBfCbZQIDEm+8EJpDFcFAAAAAMJCjIoSgYdJcb8tAAAAAISPcBslAj233G8LAAAAAOEj3EYJem4BAAAAYOgIt1GCnlsAQCRVVFQoIyNDiYmJys7OVm1t7aD7+/1+rVu3Tunp6XK73brgggu0efPmUaoWAIDhi7e6AEhdXdLRo+Y6PbcAgOGqqqpScXGxKioqNH/+fD399NMqKCjQgQMHNHPmzH6PufXWW/Xhhx/qmWee0YUXXqjW1ladOnVqlCsHAGDoXIZhGFYXcTY+n08ej0cdHR1KSkqyupyI++MfpcxMacIEyeeTXC6rKwIA54jFNmbevHm64oorVFlZ2bstMzNTt9xyi8rKyvrs//LLL+tb3/qWDh8+rIkTJw7pM2Px5wgAiA6htjEMS44Cp99vS7AFAAxHV1eX6uvrlZ+fH7Q9Pz9fdXV1/R7z4osvKicnRz/72c80ffp0XXTRRbr33nv16aefDvg5fr9fPp8vaAEAwEoMS44C3G8LAIiUtrY2dXd3Kzk5OWh7cnKyWlpa+j3m8OHD2rt3rxITE/XCCy+ora1Nd9xxhz766KMB77stKyvTQw89FPH6AQAYKnpuowBPSgYARJrrjKFAhmH02RbQ09Mjl8ulbdu26corr9QNN9ygDRs26Nlnnx2w97a0tFQdHR29S3Nzc8TPAQCAcNBzGwXouQUARMrkyZMVFxfXp5e2tbW1T29uQEpKiqZPny6Px9O7LTMzU4Zh6C9/+Yu+3M9fX91ut9xud2SLBwBgGOi5jQL03AIAIiUhIUHZ2dmqqakJ2l5TU6O8vLx+j5k/f74++OADnThxonfbn//8Z40ZM0YzZswY0XoBAIgUwq3FTp6Ujh831+m5BQBEQklJiTZt2qTNmzfr4MGDWrt2rZqamlRUVCTJHFK8fPny3v2XLl2qSZMm6Tvf+Y4OHDigPXv26Ic//KG++93vaty4cVadBgAAYWFYssXee898nTjRXAAAGK7CwkK1t7dr/fr18nq9ysrKUnV1tdLT0yVJXq9XTU1NvftPmDBBNTU1uuuuu5STk6NJkybp1ltv1cMPP2zVKQAAEDbmubXY889L/+t/SVddJb35ptXVAIDzxHIbM5r4OQIARgrz3NrE//2/5usll1hbBwAAAADYGeHWYq+9Zr5ed521dQAAAACAnQ0p3FZUVCgjI0OJiYnKzs5WbW3tgPvu3btX8+fP16RJkzRu3DhdfPHF+ud//uchFxxL/ud/pIYGc51wCwAAAABDF/YDpaqqqlRcXKyKigrNnz9fTz/9tAoKCnTgwAHNnDmzz/7jx4/XnXfeqcsuu0zjx4/X3r17tXr1ao0fP17f//73I3ISdrVnj9TTI118sZSaanU1AAAAAGBfYffcbtiwQStXrtSqVauUmZmp8vJypaWlqbKyst/9586dq9tuu02XXHKJZs2apW9/+9u6/vrrB+3tdQqGJAMAAABAZIQVbru6ulRfX6/8/Pyg7fn5+aqrqwvpPRoaGlRXV6eFCxcOuI/f75fP5wtaYlEg3C5aZG0dAAAAAGB3YYXbtrY2dXd3Kzk5OWh7cnKyWlpaBj12xowZcrvdysnJ0Zo1a7Rq1aoB9y0rK5PH4+ld0tLSwinTFj78UPp//89cv/ZaS0sBAAAAANsb0gOlXC5X0NeGYfTZdqba2lrt27dPTz31lMrLy7V9+/YB9y0tLVVHR0fv0tzcPJQyo9quXebrnDnS5MmWlgIAAAAAthfWA6UmT56suLi4Pr20ra2tfXpzz5SRkSFJuvTSS/Xhhx/qwQcf1G233dbvvm63W263O5zSbIchyQAAAAAQOWH13CYkJCg7O1s1NTVB22tqapSXlxfy+xiGIb/fH85HxxzCLQAAAABETthTAZWUlGjZsmXKyclRbm6uNm7cqKamJhUVFUkyhxQfP35cW7dulSQ9+eSTmjlzpi6++GJJ5ry3jz76qO66664Inoa9NDVJ770nxcVJ11xjdTUAAAAAYH9hh9vCwkK1t7dr/fr18nq9ysrKUnV1tdLT0yVJXq9XTU1Nvfv39PSotLRUR44cUXx8vC644AI98sgjWr16deTOwmZef918zcmRkpKsrQUAAAAAYoHLMAzD6iLOxufzyePxqKOjQ0kxkAZvv136xS+k0lLpJz+xuhoAcLZYa2Osws8RADBSQm1jhvS0ZAydYXC/LQAAAABEGuF2lL3/vtTcLI0dK4XxDC4AAAAAwCAIt6Ms0Gubmyudc461tQAAAABArCDcjjKGJAMAAABA5BFuRxH32wIAAADAyCDcjqJ335X++ldp3Dhp3jyrqwEAAACA2EG4HUWBXtsFC6SEBGtrAQAAAIBYQrgdRQxJBgAAAICRQbgdJd3d0q5d5jrhFgAAAAAii3A7ShobpY4OyeOR5s61uhoAAAAAiC2E21ESGJK8cKEUH29tLQAAAAAQawi3oyQQbq+7zto6AAAAACAWEW5HQVeXVFtrrnO/LQAAAABEHuF2FLz9tnTypDR5spSVZXU1AAAAABB7CLej4PQhyWP4iQMAAABAxBG1RgHz2wIAAADAyCLcjrBPP5Xq6sx1wi0AAAAAjAzC7QirqzMfKDV9uvTlL1tdDQAAAADEJsLtCDt9SLLLZW0tAAAAABCrCLcjjPttAQAAAGDkEW5HUGenOQ2QZD4pGQAAAAAwMgi3I6i2Vuruls4/X0pPt7oaAAAAAIhdhNsRxJBkAAAAABgdhNsRRLgFAAAAgNFBuB0h7e1SY6O5zv22AAAAADCyCLcjZPduyTCkr3xFmjbN6moAAAAAILYRbkcIQ5IBAAAAYPQQbkcI4RYAAAAARg/hdgR4vdLBg5LLJS1caHU1AAAAABD7CLcj4PXXzde5c6WJE62tBQAAAACcgHA7AgLhliHJAAAAADA6CLcjgPttAQAAAGB0EW4j7OhR6fBhKS5Ouvpqq6sBAAAAAGcg3EZYYEjylVdK555rbS0AAOeqqKhQRkaGEhMTlZ2drdra2pCOe+ONNxQfH6/LL798ZAsEACDCCLcR9sor5itDkgEAVqmqqlJxcbHWrVunhoYGLViwQAUFBWpqahr0uI6ODi1fvlyLFy8epUoBAIgcwm0EVVRIVVXmekGBtbUAAJxrw4YNWrlypVatWqXMzEyVl5crLS1NlZWVgx63evVqLV26VLm5uaNUKQAAkUO4jZAtW6Q1a8z1+++X5s+3th4AgDN1dXWpvr5e+fn5Qdvz8/NVV1c34HFbtmzR+++/rwceeCCkz/H7/fL5fEELAABWItxGwC9/Ka1aZa4XF0sPP2xpOQAAB2tra1N3d7eSk5ODticnJ6ulpaXfYw4dOqT77rtP27ZtU3x8fEifU1ZWJo/H07ukpaUNu3YAAIaDcDtMv/mN9O1vSz090ve/L23YILlcVlcFAHA61xmNkWEYfbZJUnd3t5YuXaqHHnpIF110UcjvX1paqo6Ojt6lubl52DUDADAcof15Fv165RXp1lul7m5p2TKpspJgCwCw1uTJkxUXF9enl7a1tbVPb64kdXZ2at++fWpoaNCdd94pSerp6ZFhGIqPj9err76qRf08JdHtdsvtdo/MSQAAMAT03A7R7t3SLbdIXV3S3/+9tHmzNIafJgDAYgkJCcrOzlZNTU3Q9pqaGuXl5fXZPykpSfv371djY2PvUlRUpNmzZ6uxsVHz5s0brdIBABgWem6H4K23pK9/Xfrb36Qbb5S2bZNCvEUJAIARV1JSomXLliknJ0e5ubnauHGjmpqaVFRUJMkcUnz8+HFt3bpVY8aMUVZWVtDxU6dOVWJiYp/tAABEsyH1NYYzMfzOnTu1ZMkSTZkyRUlJScrNzdUrgclgbaihQfra16QTJ6TFi6Xnn5cSEqyuCgCALxQWFqq8vFzr16/X5Zdfrj179qi6ulrp6emSJK/Xe9Y5bwEAsBuXYRhGOAdUVVVp2bJlqqio0Pz58/X0009r06ZNOnDggGbOnNln/+LiYqWmpuq6667Teeedpy1btujRRx/V73//e82dOzekz/T5fPJ4POro6FBSUlI45UbUu+9K114rtbVJV18tvfyyNH68ZeUAACIgWtoYu+PnCAAYKaG2MWGH23nz5umKK64Imgg+MzNTt9xyi8rKykJ6j0suuUSFhYX68Y9/HNL+0dBgHjokXXON1NIi5eRI//VfEm03ANhfNLQxsYCfIwBgpITaxoQ1LHmoE8OfrqenR52dnZo4ceKA+0TbxPDHjplDkFtapMsuM5+STLsNAAAAANEjrHA7lInhz/TYY4/p5MmTuvXWWwfcJ5omhj9+XFq0SGpulmbPlmpqpEFyOQAAAADAAkN6oFSoE8Ofafv27XrwwQdVVVWlqVOnDrhftEwM7/dLS5ZIhw9L559vDkUepGwAAAAAgEXCmsAm3InhT1dVVaWVK1fqV7/6lb761a8Oum+0TAz/1lvSwYPSl75kBtvp062uCAAAAADQn7B6bsOdGD5g+/btuv322/Xcc8/pxhtvHFqlFjh61Hy94gpp1iwrKwEAAAAADCasnlspvInhJTPYLl++XI8//riuuuqq3l7fcePGyePxRPBUIu/YMfP182kBAQAAAABRKuxwW1hYqPb2dq1fv15er1dZWVmDTgz/9NNP69SpU1qzZo3WrFnTu33FihV69tlnh38GIygQbum1BQAAAIDoFna4laQ77rhDd9xxR7/fOzOw7tq1aygfERXouQUAAAAAexjS05KdgnALAAAAAPZAuB1AT48UGF1NuAUAAACA6Ea4HUBLi9TVJY0ZwxRAAAAAABDtCLcDCAxJnj5dGjvW2loAAAAAAIMj3A6A+20BAAAAwD4ItwMg3AIAAACAfRBuB8ActwAAAABgH4TbAdBzCwAAAAD2QbgdAOEWAAAAAOyDcNsPwyDcAgAAAICdEG778dFH0okT5vrMmdbWAgAAAAA4O8JtPwK9tlOnSuPGWVsLAAAAAODsCLf9YEgyAAAAANgL4bYfhFsAAAAAsBfCbT8ItwAAAABgL4TbfgTC7axZlpYBAAAAAAgR4bYf9NwCAAAAgL0QbvtBuAUAAAAAeyHcnuHECam93Vwn3AIAAACAPRBuzxDotfV4zAUAAAAAEP0It2dgSDIAAAAA2A/h9gyEWwAAAACwH8LtGQi3AAAAAGA/hNszMMctAAAAANgP4fYM9NwCAAAAgP0Qbs9AuAUAAAAA+yHcnsbvl7xec51wCwAAAAD2Qbg9TXOzZBjSuHHSlClWVwMAAAAACBXh9jSBIckzZ0oul7W1AAAAAABCR7g9DffbAgAAAIA9EW5PQ7gFAAAAAHsi3J6GcAsAAAAA9kS4PU0g3M6aZWkZAAAAAIAwEW5PQ88tAAAAANgT4fZz3d3mVEAS4RYAAAAA7IZw+7kPPpBOnZLi46XUVKurAQAAAACEg3D7ucCQ5BkzpLg4a2sBAAAAAISHcPs57rcFAMSSiooKZWRkKDExUdnZ2aqtrR1w3507d2rJkiWaMmWKkpKSlJubq1deeWUUqwUAYPgIt58j3AIAYkVVVZWKi4u1bt06NTQ0aMGCBSooKFBTU1O/++/Zs0dLlixRdXW16uvrdd111+mmm25SQ0PDKFcOAMDQxVtdQLQg3AIAYsWGDRu0cuVKrVq1SpJUXl6uV155RZWVlSorK+uzf3l5edDXP/nJT/Sb3/xG//mf/6m5c+f2+xl+v19+v7/3a5/PF7kTAABgCOi5/Rxz3AIAYkFXV5fq6+uVn58ftD0/P191dXUhvUdPT486Ozs1ceLEAfcpKyuTx+PpXdLS0oZVNwAAwzWkcBvOfTxer1dLly7V7NmzNWbMGBUXFw+11hFFzy0AIBa0tbWpu7tbycnJQduTk5PV0tIS0ns89thjOnnypG699dYB9yktLVVHR0fv0hyYTw8AAIuEHW7DvY/H7/drypQpWrdunebMmTPsgkeCYRBuAQCxxeVyBX1tGEafbf3Zvn27HnzwQVVVVWnq1KkD7ud2u5WUlBS0AABgpbDD7en38WRmZqq8vFxpaWmqrKzsd/9Zs2bp8ccf1/Lly+XxeIZd8Ehoa5M+/dRcZ1QVAMDOJk+erLi4uD69tK2trX16c89UVVWllStX6j/+4z/01a9+dSTLBAAg4sIKt5G4jycUfr9fPp8vaBlJR4+arykpkts9oh8FAMCISkhIUHZ2tmpqaoK219TUKC8vb8Djtm/frttvv13PPfecbrzxxpEuEwCAiAsr3EbiPp5QjPZDKhiSDACIJSUlJdq0aZM2b96sgwcPau3atWpqalJRUZEk837Z5cuX9+6/fft2LV++XI899piuuuoqtbS0qKWlRR0dHVadAgAAYRvSA6WGeh9PqEb7IRWEWwBALCksLFR5ebnWr1+vyy+/XHv27FF1dbXSP2/ovF5v0LMynn76aZ06dUpr1qxRSkpK7/KDH/zAqlMAACBsYc1zO5z7eMLhdrvlHsXxwYRbAECsueOOO3THHXf0+71nn3026Otdu3aNfEEAAIywsHpuh3ofT7RjjlsAAAAAsLewem4l8z6eZcuWKScnR7m5udq4cWOf+3iOHz+urVu39h7T2NgoSTpx4oT++te/qrGxUQkJCfrKV74SmbMYJnpuAQAAAMDewg63hYWFam9v1/r16+X1epWVlTXofTySNHfu3N71+vp6Pffcc0pPT9fRwGOKLUa4BQAAAAB7cxmGYVhdxNn4fD55PB51dHREfJL4jg7pvPPM9c5OacKEiL49ACDKjWQb4yT8HAEAIyXUNmZIT0uOJYFe24kTCbYAAAAAYFeEW4YkAwAAAIDtEW4JtwAAAABge4Rbwi0AAAAA2B7hlnALAAAAALZHuP083M6aZWkZAAAAAIBhINzScwsAAAAAtufocPvpp9KHH5rrhFsAAAAAsC9Hh9umJvN1/HhznlsAAAAAgD05OtyePiTZ5bK2FgAAAADA0BFuxZBkAAAAALA7wq0ItwAAAABgd4RbEW4BAAAAwO4It2KOWwAAAACwO8Kt6LkFAAAAALtzbLg9dUo6ftxcJ9wCAAAAgL05NtwePy51d0sJCdK0aVZXAwAAAAAYDseG26NHzde0NGmMY38KAAAAABAbHBvruN8WAAAAAGIH4ZZwCwAAAAC2R7gl3AIAAACA7RFuCbcAAAAAYHuOD7ezZllaBgAAAAAgAhwZbnt6pKYmc52eWwAAAACwP0eG29ZWye83pwCaMcPqagAAAAAAw+XIcBuY4zY1VRo71tJSAAAAAAAR4Mhwy8OkAAAAACC2EG4BAAAAALZHuAUAAAAA2B7hFgAAAABge44Ot8xxCwAAAACxwXHh1jDouQUAAACAWOO4cPvxx1Jnp7k+c6alpQAAAAAAIsRx4TYwx+2UKdI551haCgAAAAAgQhwXbhmSDAAAAACxh3ALAAAAALA9wi0AAAAAwPYItwAAAAAA2yPcAgAAAABsz7HhdtYsS8sAAAAAAESQo8LtyZNSW5u5Ts8tAAAAAMSOIYXbiooKZWRkKDExUdnZ2aqtrR10/927dys7O1uJiYk6//zz9dRTTw2p2OFqajJfk5Kk886zpAQAAEaFXdtqAACGKuxwW1VVpeLiYq1bt04NDQ1asGCBCgoK1BRIjmc4cuSIbrjhBi1YsEANDQ26//77dffdd2vHjh3DLj5cR4+ar/TaAgBimZ3bagAAhsplGIYRzgHz5s3TFVdcocrKyt5tmZmZuuWWW1RWVtZn/x/96Ed68cUXdfDgwd5tRUVF+u///m+9+eabIX2mz+eTx+PRBx98oKSkpHDKDbJpk1RcLH3ta9Lzzw/5bQAAMcTn8yk1NVUdHR3DamOiiZ3bagAAzhRqWx0fzpt2dXWpvr5e9913X9D2/Px81dXV9XvMm2++qfz8/KBt119/vZ555hl99tlnGjt2bJ9j/H6//H5/0MlIUmpqajjlDujll6UJEyLyVgAARJVYaasBAAhXWMOS29ra1N3dreTk5KDtycnJamlp6feYlpaWfvc/deqU2gJPdzpDWVmZPB5P75KWlhZOmQAAOBZtNQDAqcLquQ1wuVxBXxuG0Wfb2fbvb3tAaWmpSkpKer/2+XxKS0sb9lCn48elQ4fMaYCYCggAIH0x1CnW2LWtBgDgTKG21WGF28mTJysuLq7PX35bW1v7/MU3YNq0af3uHx8fr0mTJvV7jNvtltvt7rN9/PjxGj9+fDglB7noInMBACCgu7vb6hIiyu5tNQAAZwq1rQ5rWHJCQoKys7NVU1MTtL2mpkZ5eXn9HpObm9tn/1dffVU5OTn93sMDAACGjrYaAOBUYU8FVFJSok2bNmnz5s06ePCg1q5dq6amJhUVFUkyhyktX768d/+ioiIdO3ZMJSUlOnjwoDZv3qxnnnlG9957b+TOAgAA9KKtBgA4Udj33BYWFqq9vV3r16+X1+tVVlaWqqurlf755LFerzdoHr2MjAxVV1dr7dq1evLJJ5Wamqqf//zn+uY3vxm5swAAAL1oqwEAThT2PLdWCMydF0tzEAIAogNtTGTwcwQAjJRQ25iwhyUDAAAAABBtCLcAAAAAANsj3AIAAAAAbI9wCwAAAACwPcItAAAAAMD2CLcAAAAAANsj3AIAAAAAbI9wCwAAAACwPcItAAAAAMD2CLcAAAAAANuLt7qAUBiGIUny+XwWVwIAiDWBtiXQ1mBoaKsBACMl1LbaFuG2s7NTkpSWlmZxJQCAWNXZ2SmPx2N1GbZFWw0AGGlna6tdhg3+VN3T06MPPvhA5557rlwu15Dfx+fzKS0tTc3NzUpKSopghdGN8+a8nYDz5ryHyjAMdXZ2KjU1VWPGcLfOUNFWDw/nzXk7AefNeQ9VqG21LXpux4wZoxkzZkTs/ZKSkhz1H1YA5+0snLezcN7DQ4/t8NFWRwbn7Syct7Nw3sMTSlvNn6gBAAAAALZHuAUAAAAA2J6jwq3b7dYDDzwgt9ttdSmjivPmvJ2A8+a8ERucem05b87bCThvznuk2eKBUgAAAAAADMZRPbcAAAAAgNhEuAUAAAAA2B7hFgAAAABge4RbAAAAAIDtEW4BAAAAALbnqHBbUVGhjIwMJSYmKjs7W7W1tVaXNKIefPBBuVyuoGXatGlWlxVxe/bs0U033aTU1FS5XC79+te/Dvq+YRh68MEHlZqaqnHjxunaa6/Vu+++a02xEXS287799tv7XP+rrrrKmmIjqKysTH/3d3+nc889V1OnTtUtt9yiP/3pT0H7xNo1D+WcY/V6V1ZW6rLLLlNSUpKSkpKUm5url156qff7sXatQVtNWx1b/5ad2FY7sZ2WnNtWR1s77ZhwW1VVpeLiYq1bt04NDQ1asGCBCgoK1NTUZHVpI+qSSy6R1+vtXfbv3291SRF38uRJzZkzR0888US/3//Zz36mDRs26IknntDbb7+tadOmacmSJers7BzlSiPrbOctSV/72teCrn91dfUoVjgydu/erTVr1uitt95STU2NTp06pfz8fJ08ebJ3n1i75qGcsxSb13vGjBl65JFHtG/fPu3bt0+LFi3SzTff3Nswxtq1djraatrqWPu37MS22onttOTctjrq2mnDIa688kqjqKgoaNvFF19s3HfffRZVNPIeeOABY86cOVaXMaokGS+88ELv1z09Pca0adOMRx55pHfb3/72N8Pj8RhPPfWUBRWOjDPP2zAMY8WKFcbNN99sST2jqbW11ZBk7N692zAMZ1zzM8/ZMJxzvQ3DML70pS8ZmzZtcsS1dhraamegrf6CE353O7GdNgxnt9VWttOO6Lnt6upSfX298vPzg7bn5+errq7OoqpGx6FDh5SamqqMjAx961vf0uHDh60uaVQdOXJELS0tQdfe7XZr4cKFMX/tJWnXrl2aOnWqLrroIn3ve99Ta2ur1SVFXEdHhyRp4sSJkpxxzc8854BYv97d3d365S9/qZMnTyo3N9cR19pJaKtpq536bznWf3c7sZ2WnNlWR0M77Yhw29bWpu7ubiUnJwdtT05OVktLi0VVjbx58+Zp69ateuWVV/Sv//qvamlpUV5entrb260ubdQErq/Trr0kFRQUaNu2bXrttdf02GOP6e2339aiRYvk9/utLi1iDMNQSUmJrr76amVlZUmK/Wve3zlLsX299+/frwkTJsjtdquoqEgvvPCCvvKVr8T8tXYa2mraaqddeym2f3dLzmynJee11dHUTsePyLtGKZfLFfS1YRh9tsWSgoKC3vVLL71Uubm5uuCCC/SLX/xCJSUlFlY2+px27SWpsLCwdz0rK0s5OTlKT0/Xb3/7W33jG9+wsLLIufPOO/XOO+9o7969fb4Xq9d8oHOO5es9e/ZsNTY26uOPP9aOHTu0YsUK7d69u/f7sXqtncpp15O2+gtOu/ZSbP/ulpzZTkvOa6ujqZ12RM/t5MmTFRcX1+cvBK2trX3+khDLxo8fr0svvVSHDh2yupRRE3jipNOvvSSlpKQoPT09Zq7/XXfdpRdffFGvv/66ZsyY0bs9lq/5QOfcn1i63gkJCbrwwguVk5OjsrIyzZkzR48//nhMX2snoq020VZ/wWnXXoqt391ObKclZ7bV0dROOyLcJiQkKDs7WzU1NUHba2pqlJeXZ1FVo8/v9+vgwYNKSUmxupRRk5GRoWnTpgVd+66uLu3evdtR116S2tvb1dzcbPvrbxiG7rzzTu3cuVOvvfaaMjIygr4fi9f8bOfcn1i53v0xDEN+vz8mr7WT0VabaKtNTv23HAu/u53YTku01aeztJ0ekcdURaFf/vKXxtixY41nnnnGOHDggFFcXGyMHz/eOHr0qNWljZh77rnH2LVrl3H48GHjrbfeMr7+9a8b5557bsydc2dnp9HQ0GA0NDQYkowNGzYYDQ0NxrFjxwzDMIxHHnnE8Hg8xs6dO439+/cbt912m5GSkmL4fD6LKx+ewc67s7PTuOeee4y6ujrjyJEjxuuvv27k5uYa06dPt/15/8M//IPh8XiMXbt2GV6vt3f55JNPeveJtWt+tnOO5etdWlpq7Nmzxzhy5IjxzjvvGPfff78xZswY49VXXzUMI/autdPRVtNWx9q/ZSe21U5spw3DuW11tLXTjgm3hmEYTz75pJGenm4kJCQYV1xxRdCjuWNRYWGhkZKSYowdO9ZITU01vvGNbxjvvvuu1WVF3Ouvv25I6rOsWLHCMAzzkfMPPPCAMW3aNMPtdhvXXHONsX//fmuLjoDBzvuTTz4x8vPzjSlTphhjx441Zs6caaxYscJoamqyuuxh6++cJRlbtmzp3SfWrvnZzjmWr/d3v/vd3t/bU6ZMMRYvXtzbYBpG7F1r0FbTVsfWv2UnttVObKcNw7ltdbS10y7DMIzI9wcDAAAAADB6HHHPLQAAAAAgthFuAQAAAAC2R7gFAAAAANge4RYAAAAAYHuEWwAAAACA7RFuAQAAAAC2R7gFAAAAANge4RYAAAAAYHuEWwAAAACA7RFuAQAAAAC2R7gFAAAAANje/wf/JqTKEB3IUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "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": 76,
   "metadata": {},
   "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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{cccc}\n",
       "\\toprule\n",
       "\\textbf{Test statistic} & \\textbf{Critical value} & \\textbf{p-value} & \\textbf{df}  \\\\\n",
       "\\midrule\n",
       "         2.118          &          9.488          &      0.714       &      4       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\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.}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_test = vecm_res.test_normality()\n",
    "norm_test.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "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": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.117800919338897, 9.487729036781154, 0.7141027804379456)"
      ]
     },
     "execution_count": 79,
     "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": 80,
   "metadata": {},
   "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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{cccc}\n",
       "\\toprule\n",
       "\\textbf{Test statistic} & \\textbf{Critical value} & \\textbf{p-value} & \\textbf{df}  \\\\\n",
       "\\midrule\n",
       "         33.52          &          48.60          &      0.491       &      34      \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\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.}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "metadata": {},
   "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33.51814228583761, 48.602367367294164, 0.4910885496410129)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_test.test_statistic, white_test.crit_value, white_test.pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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