Time_Series_Assignment_Variable_Y2_ARMA_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series and Forecasting\tAssignment\n",
    "\n",
    "### Course:\tTime Series\tand\tforecasting\tmethods\n",
    "#### Program: MSc in Data Science (part-time)\n",
    "\n",
    "\n",
    "* Student: **Perakis Georgios**\n",
    "* Code: **DS3616014**\n",
    "* Contact: **gperakis[at]aueb.gr**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data you will have to analyze are in the eclass in the **excel data-assignment.xls**\n",
    "file. The **dependent variables** for which you will construct the models you are asked\n",
    "for, are the **returns** of four investment vechicles **(Y1, Y2, Y3, Y4)** for the period **1/1991 - 12/2005**.\n",
    "\n",
    "The **independent variables** you will use in the models refer to **monthly** values/returns for the variables:\n",
    "* x1 = RUS-Rf\n",
    "* x2 = RUS (-1) -Rf (-1) lagged Russel index\n",
    "* x3 = MXUS-Rf\n",
    "* x4 = MEM- Rf\n",
    "* x5 = SMB\n",
    "* x6 = HML\n",
    "* x7 = MOM\n",
    "* x8 = SBGC-Rf\n",
    "* x9 = SBWG-Rf\n",
    "* x10 = LHY-Rf\n",
    "* x11 = DEFSPR\n",
    "* x12 = FRSI-Rf\n",
    "* x13 = GSCI-Rf\n",
    "* x14 = VIX\n",
    "\n",
    "for the period **1/1991 - 12/2005**.\n",
    "\n",
    "Analyze dependent variables based on data for the period 1/1991 - 12/2004 **[You will not use the data for the period 1/2005 - 12/2005]**:\n",
    "\n",
    "1. Construct an appropriate **time series** model (AR, MA, ARMA).\n",
    "2. Develop an appropriate **regression** model\n",
    "  * In case of **autocorrelation** problem of **regression residuals**, correct the autocorrelation problem (using time series AR, MA, ARMA models).\n",
    "  * In case of **heteroscedasticity** problem of **regression residuals**, correct the\n",
    "    heteroskedasticity problem (using time-varying ARCH, GARCH models).\n",
    "3. Write the models you have found at questions (1) - (2). Assess the **goodness of fit** of these models based on the **AIC and BIC** information criteria.\n",
    "4. Based on the estimated models of questions (1) - (2), **construct forecasts** of the analyzed series for the period **1/2005 - 12/2005**, and **evaluate the forecasts** you have found by using two evaluation criteria:\n",
    "  * the mean square prediction error\n",
    "  * the Hit ratio (indicates the percentage of predictions that correctly evaluate the sign of the actual value of the dependent variable.)\n",
    "  \n",
    "Each student will have to analyze **two only dependent variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loads the library urca, which contains the command ur.df() \n",
    "# and applies Unit-Root Testing based on Dickey-Fuller test \n",
    "library(urca)\n",
    "library(tseries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data into R can be carried out in various ways. Below, the command read.table is used:\n",
    "data_all = read.table('/home/george/AUEB/TimeSeries/time_series_in_r/Data-Assignment-Feb-2018.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th><th scope=col>V12</th><th scope=col>V13</th><th scope=col>V14</th><th scope=col>V15</th><th scope=col>V16</th><th scope=col>V17</th><th scope=col>V18</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 0.02058</td><td> 0.02058</td><td> 0.00468</td><td>0.01468 </td><td> 0.04568</td><td> 0.01745</td><td> 0.02313</td><td> 0.07273</td><td> 0.0389 </td><td>-0.0181 </td><td>-0.0946 </td><td> 0.00575</td><td> 0.01937</td><td> 0.01868</td><td> 0.0001 </td><td> 0.00004</td><td>-0.10389</td><td>-0.0547 </td></tr>\n",
       "\t<tr><td> 0.03503</td><td> 0.02583</td><td> 0.03903</td><td>0.03623 </td><td> 0.06346</td><td> 0.04568</td><td> 0.09344</td><td> 0.13201</td><td> 0.0397 </td><td>-0.0056 </td><td>-0.1147 </td><td> 0.00144</td><td>-0.00486</td><td> 0.10639</td><td>-0.0014 </td><td>-0.01039</td><td> 0.00866</td><td> 0.0032 </td></tr>\n",
       "\t<tr><td> 0.04458</td><td> 0.02168</td><td> 0.02878</td><td>0.06348 </td><td> 0.02225</td><td> 0.06346</td><td>-0.06591</td><td> 0.03187</td><td> 0.0395 </td><td>-0.0124 </td><td>-0.0407 </td><td> 0.00253</td><td>-0.04193</td><td> 0.02621</td><td>-0.0024 </td><td> 0.03228</td><td> 0.03201</td><td>-0.0435 </td></tr>\n",
       "\t<tr><td> 0.00865</td><td> 0.01325</td><td> 0.02585</td><td>0.04855 </td><td>-0.00581</td><td> 0.02225</td><td> 0.00371</td><td>-0.00174</td><td> 0.0037 </td><td> 0.0170 </td><td>-0.0224 </td><td> 0.00790</td><td> 0.01053</td><td> 0.01619</td><td>-0.0008 </td><td> 0.01107</td><td> 0.01764</td><td> 0.0136 </td></tr>\n",
       "\t<tr><td> 0.01507</td><td> 0.00227</td><td> 0.02377</td><td>0.00887 </td><td> 0.03258</td><td>-0.00581</td><td> 0.00577</td><td> 0.06795</td><td>-0.0037 </td><td>-0.0052 </td><td> 0.0282 </td><td>-0.00023</td><td>-0.00601</td><td>-0.00780</td><td>-0.0011 </td><td> 0.00235</td><td>-0.00524</td><td>-0.0231 </td></tr>\n",
       "\t<tr><td>-0.00064</td><td>-0.00924</td><td>-0.00234</td><td>0.01246 </td><td>-0.05360</td><td> 0.03258</td><td>-0.07982</td><td>-0.04322</td><td> 0.0006 </td><td> 0.0102 </td><td> 0.0010 </td><td>-0.00499</td><td>-0.01526</td><td> 0.01826</td><td>-0.0011 </td><td> 0.01085</td><td>-0.02869</td><td> 0.0362 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       " V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14 & V15 & V16 & V17 & V18\\\\\n",
       "\\hline\n",
       "\t  0.02058 &  0.02058 &  0.00468 & 0.01468  &  0.04568 &  0.01745 &  0.02313 &  0.07273 &  0.0389  & -0.0181  & -0.0946  &  0.00575 &  0.01937 &  0.01868 &  0.0001  &  0.00004 & -0.10389 & -0.0547 \\\\\n",
       "\t  0.03503 &  0.02583 &  0.03903 & 0.03623  &  0.06346 &  0.04568 &  0.09344 &  0.13201 &  0.0397  & -0.0056  & -0.1147  &  0.00144 & -0.00486 &  0.10639 & -0.0014  & -0.01039 &  0.00866 &  0.0032 \\\\\n",
       "\t  0.04458 &  0.02168 &  0.02878 & 0.06348  &  0.02225 &  0.06346 & -0.06591 &  0.03187 &  0.0395  & -0.0124  & -0.0407  &  0.00253 & -0.04193 &  0.02621 & -0.0024  &  0.03228 &  0.03201 & -0.0435 \\\\\n",
       "\t  0.00865 &  0.01325 &  0.02585 & 0.04855  & -0.00581 &  0.02225 &  0.00371 & -0.00174 &  0.0037  &  0.0170  & -0.0224  &  0.00790 &  0.01053 &  0.01619 & -0.0008  &  0.01107 &  0.01764 &  0.0136 \\\\\n",
       "\t  0.01507 &  0.00227 &  0.02377 & 0.00887  &  0.03258 & -0.00581 &  0.00577 &  0.06795 & -0.0037  & -0.0052  &  0.0282  & -0.00023 & -0.00601 & -0.00780 & -0.0011  &  0.00235 & -0.00524 & -0.0231 \\\\\n",
       "\t -0.00064 & -0.00924 & -0.00234 & 0.01246  & -0.05360 &  0.03258 & -0.07982 & -0.04322 &  0.0006  &  0.0102  &  0.0010  & -0.00499 & -0.01526 &  0.01826 & -0.0011  &  0.01085 & -0.02869 &  0.0362 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | \n",
       "|---|---|---|---|---|---|\n",
       "|  0.02058 |  0.02058 |  0.00468 | 0.01468  |  0.04568 |  0.01745 |  0.02313 |  0.07273 |  0.0389  | -0.0181  | -0.0946  |  0.00575 |  0.01937 |  0.01868 |  0.0001  |  0.00004 | -0.10389 | -0.0547  | \n",
       "|  0.03503 |  0.02583 |  0.03903 | 0.03623  |  0.06346 |  0.04568 |  0.09344 |  0.13201 |  0.0397  | -0.0056  | -0.1147  |  0.00144 | -0.00486 |  0.10639 | -0.0014  | -0.01039 |  0.00866 |  0.0032  | \n",
       "|  0.04458 |  0.02168 |  0.02878 | 0.06348  |  0.02225 |  0.06346 | -0.06591 |  0.03187 |  0.0395  | -0.0124  | -0.0407  |  0.00253 | -0.04193 |  0.02621 | -0.0024  |  0.03228 |  0.03201 | -0.0435  | \n",
       "|  0.00865 |  0.01325 |  0.02585 | 0.04855  | -0.00581 |  0.02225 |  0.00371 | -0.00174 |  0.0037  |  0.0170  | -0.0224  |  0.00790 |  0.01053 |  0.01619 | -0.0008  |  0.01107 |  0.01764 |  0.0136  | \n",
       "|  0.01507 |  0.00227 |  0.02377 | 0.00887  |  0.03258 | -0.00581 |  0.00577 |  0.06795 | -0.0037  | -0.0052  |  0.0282  | -0.00023 | -0.00601 | -0.00780 | -0.0011  |  0.00235 | -0.00524 | -0.0231  | \n",
       "| -0.00064 | -0.00924 | -0.00234 | 0.01246  | -0.05360 |  0.03258 | -0.07982 | -0.04322 |  0.0006  |  0.0102  |  0.0010  | -0.00499 | -0.01526 |  0.01826 | -0.0011  |  0.01085 | -0.02869 |  0.0362  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  V1       V2       V3       V4      V5       V6       V7       V8      \n",
       "1  0.02058  0.02058  0.00468 0.01468  0.04568  0.01745  0.02313  0.07273\n",
       "2  0.03503  0.02583  0.03903 0.03623  0.06346  0.04568  0.09344  0.13201\n",
       "3  0.04458  0.02168  0.02878 0.06348  0.02225  0.06346 -0.06591  0.03187\n",
       "4  0.00865  0.01325  0.02585 0.04855 -0.00581  0.02225  0.00371 -0.00174\n",
       "5  0.01507  0.00227  0.02377 0.00887  0.03258 -0.00581  0.00577  0.06795\n",
       "6 -0.00064 -0.00924 -0.00234 0.01246 -0.05360  0.03258 -0.07982 -0.04322\n",
       "  V9      V10     V11     V12      V13      V14      V15     V16      V17     \n",
       "1  0.0389 -0.0181 -0.0946  0.00575  0.01937  0.01868  0.0001  0.00004 -0.10389\n",
       "2  0.0397 -0.0056 -0.1147  0.00144 -0.00486  0.10639 -0.0014 -0.01039  0.00866\n",
       "3  0.0395 -0.0124 -0.0407  0.00253 -0.04193  0.02621 -0.0024  0.03228  0.03201\n",
       "4  0.0037  0.0170 -0.0224  0.00790  0.01053  0.01619 -0.0008  0.01107  0.01764\n",
       "5 -0.0037 -0.0052  0.0282 -0.00023 -0.00601 -0.00780 -0.0011  0.00235 -0.00524\n",
       "6  0.0006  0.0102  0.0010 -0.00499 -0.01526  0.01826 -0.0011  0.01085 -0.02869\n",
       "  V18    \n",
       "1 -0.0547\n",
       "2  0.0032\n",
       "3 -0.0435\n",
       "4  0.0136\n",
       "5 -0.0231\n",
       "6  0.0362"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(data_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2 <- data_all$V2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Let create a time series object using the function “ts” from a vector - single time-series or a matrix - multivariate time-series. The data consist of the Y2 monthly returns from 1/1991 - 12/2004, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Jan      Feb      Mar      Apr      May      Jun      Jul      Aug\n",
       "1991  0.02058  0.02583  0.02168  0.01325  0.00227 -0.00924  0.00966  0.00053\n",
       "1992  0.05392  0.02265 -0.01136  0.01804  0.01633  0.02156  0.01779  0.00341\n",
       "1993  0.02083  0.00650  0.03433  0.02154  0.02199  0.01372  0.01501  0.01853\n",
       "1994  0.02228  0.00153  0.00033 -0.00750 -0.00497 -0.00133  0.00716 -0.00250\n",
       "1995  0.00780  0.00595  0.00140  0.01080  0.00274  0.01248  0.00715  0.00886\n",
       "1996  0.01039  0.01013  0.00721  0.01611  0.01309  0.00560 -0.01023  0.01089\n",
       "1997  0.00951  0.00655 -0.01105  0.00863  0.01418  0.01499  0.01133  0.00994\n",
       "1998  0.01548  0.00946  0.01140  0.01515 -0.00118 -0.00265 -0.00933 -0.06208\n",
       "1999  0.02238 -0.00282  0.00195  0.02421  0.00782  0.00972  0.00434  0.00385\n",
       "2000  0.01347  0.00787  0.01310  0.00926  0.00210  0.01691  0.00413  0.01065\n",
       "2001  0.01624  0.00647 -0.00098  0.00436  0.00634 -0.00532  0.00405  0.00569\n",
       "2002  0.00883 -0.00108  0.00481  0.00444  0.00066 -0.00329 -0.00340  0.00543\n",
       "2003  0.00934  0.00602  0.00619  0.01297  0.00890  0.00410 -0.00039  0.00290\n",
       "2004  0.01135  0.00473  0.00353 -0.00590 -0.00428  0.00052  0.00533  0.00698\n",
       "          Sep      Oct      Nov      Dec\n",
       "1991  0.01592 -0.00923  0.00288 -0.01519\n",
       "1992  0.00102  0.00258  0.01462  0.00848\n",
       "1993  0.01092  0.01553  0.00828  0.02489\n",
       "1994 -0.00679 -0.00300 -0.00438 -0.00535\n",
       "1995  0.00950 -0.00099  0.01219  0.01217\n",
       "1996  0.00380 -0.00259  0.00593  0.01461\n",
       "1997  0.01434  0.00527  0.00874  0.00454\n",
       "1998 -0.00173 -0.00841  0.01282  0.00258\n",
       "1999  0.00078  0.00105  0.00637  0.01075\n",
       "2000  0.00393 -0.00981 -0.00618  0.00149\n",
       "2001  0.00043  0.00679  0.00392  0.00456\n",
       "2002  0.00157  0.00288  0.01018  0.00601\n",
       "2003  0.00783  0.00983  0.00404  0.01154\n",
       "2004  0.00220  0.00154  0.00888  0.00568"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "j2 = ts(y2, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "j2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note:\n",
    "Since we are talking about **returns** we **don't** need to take to logs and the differences of the logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methodology\n",
    "\n",
    "In order to work we will use the **Box-Jenkins methodology** that constists of the following steps:\n",
    "* **Identification step**\n",
    "  * Use autocorrelation and partial autocorrelation of the series to find out appropriate values of p and q\n",
    "* **Estimation step**\n",
    "  * Estimate the models parameters using Maximum Likelihood method and least squares method\n",
    "* **Diagnostic checking step**\n",
    "  * Examine if the chosen (estimated) model fits the data reasonable well - test if the residuals of the estimated model are uncorrelated, homoscedastic and normal, i.e. white noise\n",
    "* **Forecasting step**\n",
    "  * Compute forecasts based on the fitted model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The time series plot and the histogram for the Y3 data is given by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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StVSiEpzOrV+OcfNGmih1s/egQ3N3lmZldXDuzYR+PDB8TEQHGjRbGoKLi54dGj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\nAACFINgBAAAoBMEOAABAIf4fM5MdX8jpCVwAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Histogram of Y2”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the graphics\n",
    "par(mfrow=c(2, 1))      \n",
    "plot(j2, type=\"l\", col='red', lwd=1, main=\"Time Series plot of Y2\", ylab=\"Monthly returns\")\n",
    "hist(j2, nclass=20, main=\"Histogram of Y2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note:\n",
    "The **autocorrelation** and **partial autocorrelation** plots are useful to examine if there is **dependence between lagged values **of the analyzed series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqAgICsrCxq09HRMT09/ZVXXqE2T58+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WrcrLy0tISFBoJoFAYG1tjd+VAUCh/Pz8\n/Pz8CCHXrl2Li4szNTVlOpFKunr1amNjI9Mp+q/a2tq8vLzKyspeFnYikUhPT0/+15vMzExf\nX18Udiqnx6uUODs779q1SxFRAAAYER0drehriAFYbtGiRbt372Y6BfSB7p6xAwDgqqioqLlz\n5yr/V1Ysnwjs0dra2uFTKUDloLADgP5u3bp1u3btioyMHDJkiHKOiOUTAUBBUNgBQH8nlUpt\nbW1HjRo1ZcoUa2trPv+fifGjjz7q88Nh+UQAUBwUdgDQ31GXFhkbG1+9evXq1avyXYoo7LB8\nIgAoDgo7AOjvhEKhkg+H5RMBQEF6fFcsAEB/kJ2draAHPWD5RABQHJyxA4D+rqKiIjo6+urV\nq/JPri8tLR0wYIAiDoflE7sjOzs7LS1t27ZtTAcBUDE4YwcA/V10dHRaWpq3t3dDQ4Ourq6v\nr6+FhYWamtqpU6cUcThq+URra+uNGzcGBAT4+Pj4+PjMmjVrw4YNlpaWZ86cmTx5siKOq1ou\nX76cnJzMdAoA1YMzdgDQ3/3666/R0dGbN29OTk5OTk6Oi4sjhAQEBJw/f97e3l4RR8TyifDc\ndu/eHRoaioVSoDM4YwcA/V11dfXIkSMJIePGjfvzzz+pxsWLF+/fv1+hx6WWT/Ty8vL29ra1\ntUVVx23Xrl0LCQnp/X42b9588eLF3u8Hnk9SUlJAQADTKbqCM3YA0N9ZWVnl5uaGhIRYWFjU\n19dfv37dwcFBTU3txo0byg9TXFzc0NDg4ODQncGlpaVlZWWd9cpkMpFIdPny5S4GCIVCeo1R\nkUhECMnPz3/8+DE9oKqqiroKkPLgwYPGxkb5fT5+/Li5uVm+pba2VltbW0NDg26prKwcMGAA\nj8ejWyoqKoyMjOhN6kd9+fJlNbW/TzeUlJS0221LS0tra6uurq78gcrKyuTHVFVVGRgY0Dtp\naWkhhBQVFck/m7DdoaVSaXV1tfyf8cmTJy0tLfKrpjY0NKirqwsEArqlpqZGV1dXXV29s90+\n3ZKVlZWRkSGftrS0tKGhQb6loqKC+jnIH7q0tFS+RSKR3L59m2559OgRIeT69et1dXWdHVoi\nkdTX1xsYGNAtzc3NEonkhRdeoFvq6urEYrH8gVpbW+/evUu3yGSy/fv3v/nmm/RvIEVFRYSQ\nnJwcLS0t+odZU1Mjf3FqWVlZbW2t/G7r6+s1NDTotxBCqqur9fT0evTDvHPnTltbm/xuRSJR\nfX29fEtlZWW7z2pjY+ODBw/kW6RS6a1bt+gWsVhMCLl27VplZSXVIhQKpVKp/FsuXrxYVFTU\nxX9WDx48aGtr66xXGWT9D4/H27hxYxcDtLS0fv75Z6XlAegPdu3aNXr0aKZTdGzdunU6Ojp7\n9+6VyWSTJ0+eO3fuH3/8MXXqVGdnZ+WHmTp1avdnZmdnZyb//QCAjhgaGip0lugaztg9m1gs\n3rlz5549e5gOAgAKsWHDBrFYfOnSJULIxx9/7O3tnZiYqK2tnZKSovwwQUFBjo6O3Rx88eLF\npqamznqNjIxWrly5Zs2azga0trbKn1d7uuXXX39duHBhaWlpF2OkUqlMJpM/3RIYGDhlypTI\nyEi6xdbWNj4+3t/fn9p8/PixnZ3d77//PmLEiO6HWb9+/b179xISEuiWtrY2dXV1+ROBo0aN\nWrNmzdy5czvbydWrVydNmnTv3j36hFxycvLmzZuvX79Oj/noo48uXbr0ww8/0C0LFy4cPHhw\nTEwM3eLp6fnWW2+9/fbbdMugQYNSUlImTJhAbT569GjSpEl//PGH/Jm/Z/4ZJRIJIUT+h/nM\ntzzdkpWVNWfOHOpMHuXrr78+ePDghQsX6JbVq1c/fPjw0KFD8jvh8/nyP8znODQhpKWlRf5s\nnEwmk0gk8mdM58+fb2VltWPHDrpl9OjRkZGRb775JrUplUqNjY1PnTrl6elJtRQVFXl6et68\neZM+u9ydMBKJhMfj0advn/tP1J0WeXv37j158mRnvUrArsJOIpH85z//KSgoeOmll/z8/OQ/\n3Pn5+ZcuXVq0aJHyUxUVFe3duxeFHQBXaWtrHzx4kHrt4uJSXl5+5cqVYcOGDR48WPlhIiIi\nuj9YIBDIfz/Y4YDePLRFV1eXx+P1dA/Jycl6enrywXg83gsvvEDvh/qiSl9fv0d7FggEGhoa\nXb9FTU1N/kBP09fXJ4QYGhpSLwghr776aktLi/xbBAIBn8+Xb9HU1NTS0pJvUVdX19HRaXcg\nPT09umXAgAHtCmKloUpJ+WyhoaH29vbyLVpaWpqamgp6oE/XNDQ02n0s2/0wqacOyf8w6b81\nRgL3lLa2tnx9rHwsKuyqq6v9/f3Pnz9PbTo7O6enp7/44ovU5unTp1esWMFIYQcAHFZfX3/7\n9m0TExMzMzOqRUdHx9vbWwmHFgqFIpFILBa3tbWZmZlZWVnZ2toq4biKNmjQIKYj9MDQoUPf\ne+89+RZNTU1NTU2m8iiChYWFhYUF0ylASVh0V+z69evz8vJSU1MfPXqUmppaWlo6e/Zs6rw0\nAECfa2lpiYiI0NfXd3FxMTc39/T0LC4uVs6hjx8/7ubmZmdnN3HixLlz5y5YsGDq1KnDhg1z\nd3dPTU1VTgbozL///W/57yiBcWZmZjNnzqRPskLXWFTYnTp1as2aNUFBQcbGxkFBQSdOnMjO\nzv7888+ZzgUA3PThhx/u37//tdde+/TTT5ctW3blypU5c+bILz6hIImJiaGhoYMHD05ISMjN\nzS0pKSkpKcnNzU1MTLSysgoJCcGDeZmlo6NjYmLS9Rh3d/dhw4YpJw8MHDjw5MmT8pfuQRfY\n9VWstbU1venp6RkREbFly5Z58+bJ34gOANAnjh079tprr33//ffUpp2d3ZIlS27cuKGghxLT\n4uLiFixYcOTIEflGS0vLUaNGhYaGRkVFxcfHBwcHKzQD9NLhw4eZjgDQMRadsXNxcZG/C4kQ\nsm3bNi0trbCwMJlMxlQqAOCqO3fuvPLKK/Tmq6++SggpKSlR9HGFQiH1TJMO+fr6FhQUKDoD\nAHAViwq74ODgpKSkmTNn7t27t76+nhBiaGh48ODBX375JSAgIC8vj+mAAMApbW1t8g9olX/y\nrUI5OTklJye3trY+3SWVShMTE11dXZWTBAC4h0VfxS5durSxsTEuLu6nn3569dVXhw8fTgh5\n9dVXT548+e677yrh12gAACXYunWrr6+vh4dHSEiIk5MTdalJVVVVQUFBSkpKfn5+eno60xkB\nQFWxqLDj8XirVq1auXJlWVmZ/Moh06ZNu3Pnzp9//nnr1i0G4wEA9zQ3Nzc0NFCvqRdNTU10\nCyFE/pReX5kyZUpWVlZsbOzGjRvl79Xg8/n+/v579uyhn8sKANBTLCrsKDwej36aFE1dXd3L\ny8vLy4uRSADAVYsXL168eLF8S2BgoPymgi7wpSa05uZmsVgsEolkMpmpqam5uTm9CicAwPNh\nXWHXmR4tjA0A8Ezbt29nNoBAILC2tpZ/GgDbMPsAfdViaGhoYGDAdAoA1SnswsPDMzMzu/nb\nc2BgYBc3W8hkMvlF9ACgf1q/fj3TEdhu7Nixu3fvZjqFahCLxV2sHwqgNCpT2PVoYewlS5bc\nvXu3s953333X0NCwj3IBAHDWwIEDw8PDmU6hGlDVAUuoTGHXo4Wx5Z9N9bTFixfjv0AAAADg\nHjYWduxfGPubb74pLCzctWsX00EAAAAA/sGuwu748eMxMTE5OTnt2t3c3NatWzd79mxGUj3t\n5s2b+fn5TKcAAAAA+B8sWnkCC2MDAAAA9AaLzthhYWwAAACA3mDRGTssjA0AAJ3R1tbW1tZm\nOoUKMDAwUOknP/B4PD09PT09PaaDqCoWnbGjFsaeO3fu07esYmFsAIB+7oMPPmhra2M6hQrw\n8PAQi8VMp3h+PB6vvLxcIBAwHURVsaiwU+LC2CtOnfIvK/t7Q1OTfPABMTH5ezMujrS17fu/\n/3P4/vu/eydP1qTfGRdHhEJy6VJgRcX4d9/t4L1C4T+HQS960Uv3DhyIxbKgV/Avffep+iO9\n8HfdGywq7JS2MLZMZtLcrF1V9femujppbv6n9+FDIpMZ1tXxqQHq6uTJEzX53qoq0tys3dqq\nW1XVwXvp3Xa4Z/Sit9/26uuzaLYBJfP39x8+fDjTKQD6Bxn7NDU13blz5/z58+fOnRMKhQ0N\nDX27fx6Pt3Hjxi4GaGlp/fzzz/TmmTNneDye/IA1a9ZMmzatb1MBcNuuXbtGjx7NdIr+5Zlz\nHVPKy8sJIdeuXevzPVtZWR06dKiLAZcvXyaE1NTU9Gi3c+bMiYyM7F00domMjJwzZw4jhw4I\nCIiOjpZvsbOz++KLLxgJowiMz3Vs/B2a/QtjAwAAALAQi+6KBQAAAIDeQGEHAAAAwBEo7AAA\nAAA4AoUdAAAAAEegsAOAHqutrV24cGFLSwvTQQAA4H+gsAOAHhOJRIcPH66Se1RdZmbmsmXL\nGIwEAN03Y8aMoKAgplOAQqCwA4BnCwwMzMrK6mLAtWvXuh4AAOwxffr0uXPnMp0CFAKFHbBX\nXV1dUVER0ymAEEJycnKKi4uZTgEAAM+Awg7YKyEhQXV/p8zPz//pp5+6HnP8+PHW1lbl5AGA\n5zZ//nx8cQmqAoUdsFdbW5tEImE6xXNKTk6Oj4/vYkBVVVVoaCg7T0mWl5eLxWKmUwCwRUBA\nwKRJk5hOAdAtKOxAtXl5eRUWFjKd4nnIZDL6/9lm69atq1evZjoFAAD0GAo7UG2XLl0qLS1l\nOgXXtLW1tbW1MZ0CoO/Z2NisXLlSV1eX6SAAioLCDrhG/hkcAADyDAwMdu3apaaGf/uAs/Dh\nVoiGhoZRo0Y9evSI6SBcU1BQcP/+/S4G/PHHH0OHDlVWHKz2IRAAABlySURBVAAAAHbhMx2g\nA0KhUCQSicXitrY2MzMzKysrW1tbpkP1TF1dXV5eXkVFxaBBg5jOwilr164dMWJETExMZwMa\nGhoaGxuVGan7Fi9ePGPGjFmzZjEdhNy5c+ell15iOgUAAPQ9dp2xO378uJubm52d3cSJE+fO\nnbtgwYKpU6cOGzbM3d09NTWV6XRdCQkJ+eGHH7oe8+TJE+WE6akpU6acPXuW6RTdIpVKpVIp\n0yme05UrV27cuMF0CvLkyRM7O7ucnBymgwD0PYFAoK2tzXQKACax6IxdYmLivHnzpk2blpCQ\n4OjoOHDgQEJIZWVlYWFhUlJSSEjIsWPHgoODmY7ZMaFQeO/evS4G/PDDD1u2bMnNzVVapO4r\nKip68OAB0ykU5dChQ7dv3962bRvTQdhCIpFIJBLW/prRH/j4+DxzzLlz55SQhHvOnTtH/dsB\n0G+xqLCLi4tbsGDBkSNH5BstLS1HjRoVGhoaFRUVHx/P2sLumWpqampqaphO0R9dv36dnc+K\ng35r0aJFO3fuvHHjhr29vY2NDdNxOAVXvwCwqLATCoVRUVGd9fr6+h4+fFiZeQAAFGHhwoUz\nZsywsrJatGjR8uXLmY4DAJzComvsnJyckpOTO1xhSSqVJiYmurq6Kj+V4rS2tvb0UWHp6ek7\nduzo0VsKCwtXrFjRo7cAgKKZmJiMGTOG6RQAwEEsOmO3detWX19fDw+PkJAQJycn6jqJqqqq\ngoKClJSU/Pz89PR0pjP2paVLlxoaGu7atav7b/nrr79+++239evXd/8teXl5CQkJcXFxPQ8I\n/cWtW7c+++yzTz75hOkg/csnn3xiYGDAdAoA4BoWFXZTpkzJysqKjY3duHGj/J2PfD7f399/\nz549np6eDMbrc3V1derq6kyn6I8qKyv19fX5fBZ9+JmVn59/6NAhFHZK5u7uznQEAOAgdv3b\n5uXl5eXl1dzcLBaLRSKRTCYzNTU1NzfX0dFhOhpwx7Rp0959991FixYxHQSgn9LU1OTxeFpa\nWkwHAeAgFl1jRxMIBNbW1l5eXt7e3ra2tqjqupCbm1teXs50ChXT1NTU1NQk35Kdnd3Lfd68\neXP48OGq+4w9YI/i4uLr168znUKxDAwM7t69q3JPngdQCWws7DrUHya75xAZGXno0CGmU6i2\nsrKy0aNHFxcX9+hdoaGhhYWF9OajR49u3rwpkUj6OFzP3b1799dff2U6BTy/8PBwR0dHplMo\nnJWVFdMRgBnTpk2bMGEC0ym4jCeTyZjO0C2vvPJKZmZmN9MuWrTo6tWrnfVevnzZxMTE0tKy\nswE5OTk2Njb6+vrUZl1d3c2bN+UviCktLW1qapL/dbOwsNDIyMjExITabG1tzcvLc3BwEAgE\nVEtFRYVYLJafr+/evauurj5kyBC6pbi42NTUlH5La2vr/fv35Zd+EovFdXV1dnZ2dMuNGzcM\nDAxMTU2pTZlMdufOHSsrK/oCsqqqqvv37zs7O9Nvefz4MSHE2NiYbsnLy7OwsKCf6tnW1nbv\n3r2XXnqJx+NRLdXV1TU1NfITcVlZmba2tvyl33fu3LG0tNTQ0KA2m5uby8rK5JdtraysbGtr\no39EhJD79+8PHDjwhRdeoDYlEklxcbG1tTW9Pnd5efnjx49HjhxJv+XWrVsCgcDCwoJuuXLl\nyrBhw/T09KjNuro6oVDo5uZGD3jw4EFzc7P8X1ZBQcGgQYPo511Rf1mOjo70F0MNDQ2VlZXy\nn5Dy8nI+ny//4NPc3Fxra2v6J1BfX3/jxg03Nzf6hyYSiRoaGoYNG0a/paioaMCAAYMHD6b/\nvLm5uSNHjqQflN/U1PTo0SP5j8TTf1n37t0zMTGh3/L0X1ZZWVlNTc3w4cPpt4jFYh0dHTqq\nVCrNyckZMWIE/ZOvrq6+d+/eqFGj6LeUlJRIJBJra2u6JT8/39zc3MjIiNpsbm6+fv26s7Mz\n/TdeXl5eUVFhb29POvHw4UOJRCISiTobAJTPP//85s2b8fHxvd+VmpraBx98sGXLlt7vCkBx\nhg8fvnz58nfeeYfpIH0jNjY2OTn50qVLTAVg1zV2XQgKCur+b7G+vr7y1U87ubm5EyZM6OLK\nZTs7uxEjRtD/zD958uT69evyD1t59OhRXV2dfMl148aNwYMHGxoaUpsymczOzs7V1ZW+PaK+\nvr6kpES+Rrl//766urq5uTndsmXLFjc3NwcHB3rAvn37XnvtNfrfzoqKiqqqKvkaRSgUGhkZ\n0QVHc3Pz5s2bw8LC6N02NDTcvXtX/keXkpIilUrlH/Vsb29vbW1N/zMvFov37Nkzc+ZM+kvw\nqqqqR48eyf9IDxw4YGVl9corr1CbEolk/fr1oaGhdPFXWFjYbqWQtLS0+vp6+ZbY2FgHB4fR\no0dTm48fP46Li5s+fTpdUtfU1JSVlcnXKHfv3n3hhRfkq8Nhw4bZ29vTf1ktLS2FhYUuLi70\ngPLy8oaGBvkapaioyMzMjC50qL8sNzc3uqD866+//vvf/8pH/e6773R1dQMCAuiW4cOHDxs2\njC6wWltb8/Pz5QvKx48f19TUyD9+9ubNm4MGDRowYIB8eBcXF/pDkpubm56eLn/cpKQkNTW1\nOXPm0C3bt293cXGhK/XS0tK9e/cGBATQP4HKysqKigr5gvLLL78cOnTo1KlT5Y/r5OREf66a\nmppu3brl5OREDxCJRBKJRL60HTlypJWVla6uLrUpkUhycnLc3d3lq/+HDx/K/2W1c/bs2Vu3\nbnXWC7SIiIjuD66srKyurlZcGABQPbL+R1dX9+TJk0yn6IChoeEPP/xAb/7555+EkMbGxu7v\ngZrir1y50sWYt956KywsrIsB1MnOioqKLsZMnjx548aN9GZLSwsh5MKFC3TLiRMn9PT05N+y\ndOnS4OBg+RYbG5uvvvqK3rx58yYh5MGDB10cVzm++uorGxsb+Zbg4OClS5cq+rjfffedubm5\nfEtYWNhbb70l32JiYnL8+HF68/Lly4SQmpqaLnY7YcKEzZs3923U57Br167Ro0cznYJdbt68\n+d///jcxMfHbb7/NzMwUCoU93YN8Od4hJXxuAXrJzs7uiy++YDpFn2F8rmPjGTuhUCgSicRi\ncVtbm5mZmZWVFa6x7SZNTU1tbW36e0lQLZqampqamn2+WzU1NfpkJLDE8ePHY2JicnJy2rW7\nubmtW7du9uzZ3dzPuXPnKioqOut1cnKaNm3a86cEABXErsKurya7fktbW7uyspK+So/lgoOD\nVWU1kfnz5yuhXH7ttdc8PDz6fLdxcXH0VZjABomJifPmzZs2bVpCQoKjoyN1KUVlZWVhYWFS\nUlJISEi7yxi6YGBg0MVTjlHQA/RDLCrs+nCyU1G+vr69PzepKlUdIeSjjz5iOkJ3zZo1SwlH\n4fP58reb9BX5K/+ADeLi4hYsWHDkyBH5RktLy1GjRoWGhkZFRcXHx3N7rgMAxWFRYYfJ7tix\nY0xHAACFEwqFUVFRnfX6+voePnxYmXkAmDV9+vRnXi0K3ceiE/VCoVD+xr12fH19CwoKlJkH\nAEARnJyckpOTW1tbn+6SSqWJiYmqcokCQJ/YvXv3uHHjmE7BHSw6Y0dNdnPnzqWfwkDDZMc2\nNjY2ff58USsrq/j4eFwNBpy3detWX19fDw+PkJAQJycn6rKTqqqqgoKClJSU/Pz89PR0pjMC\ngKpiUWGHyY4lTE1NX3nlFfqJZR368ssvu96JoaEh/VS/btLU1Fy2bFmP3qIgXl5eS5YsYTpF\nt/D5fB6PRz8JD1TClClTsrKyYmNjN27cKL8MHZ/P9/f337Nnj6enJ4PxAEClsaiww2THEiYm\nJqdPn+7lTnx8fO7cudMneZRvxIgRI0aMYDpFtzg5OeXm5tIPlwZV4eXl5eXl1dzcLBaLRSKR\nTCYzNTU1NzfH0tgA0EssKuwIJjtuoZc1o+jq6jY2NjIVhqt4PJ78enGgWgQCgbW1tfyyKAAA\nvcSuwo6CyU6hBAJBW1tb3+5TXV3dxMREfrGsp23fvl0ikfTtcQG4QSqV+vn57du3T36hQgCA\n58DGwg4UKiYmRiaT9e0+1dXVHz582PUYPp/f7hweAFBkMtnPP/9cV1fHdBAAUHn4h7bf6fqu\nCAAAAFBdLHqOHQC0w+PxeDyefIu+vr6+vj5TeQAAgOVwxg6AvVatWtVuuc9r165paWkxlQcU\nRF1d/fz5871fURAAAIUdAHuNHDmyXQuqOq7y8vJiOgIAcAG+imUvfX39F154ATccAAAAQDeh\nsGMve3v7ioqKpxdYAwAAAOgQCjtWw/duAAAA0H0o7AAAAAA4AoUdAAAAAEegsAMAAADgCBR2\nAAAAAByBwg4AAACAI1DYAQAAAHAECjsAAAAAjkBhBwAAAMAR/bGwa2y85O8/k8cj1P80NUlO\nzj+9jo6E7kIvetHbV72lpSYElAtzHXrRq/xexuc6nkwmYzaB8r3wwti1a/e8/PLL1CafT3x8\niLr6373XrpGysn8Goxe96O2T3osX41JTky5dukRAWTDXoRe9yu9lfK7rj4Wdnp5eYmLizJkz\nmQ4C0I/ExsYmJyejsFMmzHUAysf4XNcfv4oFAAAA4CQUdgAAAAAcgcIOAAAAgCNQ2AEAAABw\nBAo7AAAAAI7gMx2AGbdu3bp8+XJnvVeuXBEIBDweT5mRnk9NTY2Ojo6GhgbTQZ6tqalJIpHo\n6uoyHeTZJBJJbW3tgAEDmA7SLfX19aNHj2Y6xbOVlpYyHaE/kp/rbty40dLSohLTBVG1/wwJ\nIY8ePTI2NlaJfziISk3IlLq6ujFjxjCdolsYn+v6Y2FnbGwcHR3NdAqAfmf69OlMR+hfMNcB\nMILZua4/PsfumQQCQVpamq+vL9NBns3T09Pf33/t2rVMB3m2yMjI8vLypKQkpoM829dff/3R\nRx/dunWL6SDPduXKFXd395qaGn19faazANu5urq++eaby5YtYzpIt3zxxRfx8fFFRUVMB+ku\nPp//yy+/TJkyhekg3fLee++VlZUlJyczHaRbrl+/7ujoWF5ePmjQIKazqABcYwcAAADAESjs\nAAAAADgChR0AAAAAR6CwAwAAAOAIFHYAAAAAHIHCDgAAAIAjUNgBAAAAcAQKOwAAAACO6I8r\nTzzTkCFDVOUpiObm5mZmZkyn6BYzMzN1dXWmU3SLqamphYUF0ym6ZeDAgebm5lpaWkwHARXw\n4osvmpqaMp2iu1ToP0OKlZWVqvzDQQgxMzNTldXPCCGGhoZmZmY6OjpMB1ENWHkCAAAAgCPw\nVSwAAAAAR6CwAwAAAOAIFHYAAAAAHIHCDgAAAIAjUNgBAAAAcAQKOwAAAACOQGEHAAAAwBEo\n7AAAAAA4AoUdAAAAAEegsAMAAADgCBR2AAAAAByBwg4AAACAI1DY/Y8zZ854e3sbGBh4eHgk\nJiYyHadjhw4d8vHxadfItuSfffbZpEmTDA0Nhw4dGhkZWV1dTXexKmpTU9P69evt7Ox0dHSG\nDx++bdu2lpYWupdVUeWtXr3a0tJSvoW1UYENVOXjoRKTG0VVpjiisrMcwUT33GTw/50/f15d\nXd3Pz+/QoUPz588nhBw7dozpUO2Vl5ePGDHC29tbvpFtyWNiYgghISEhR48e3b59u76+vre3\nt0QiYWHUd955h8/nr169Ojk5efXq1erq6suWLaO62BaVdvbsWTU1NQsLC7qFtVGBDVTl46ES\nkxtFhaY4mWrOcjJMdL2Awu4fAQEBzs7OT548oTanTZvm6urKbCR5ly9fnjhxokAgIIS0m/tY\nlVwqlQ4aNMjPz49uSU1NJYT88ssvMpZFbWxsVFNT27JlC92ycOFCIyMj6jWrotJqa2utra31\n9fXl5zt2RgWWYP/HQ1UmN4oKTXEy1ZzlZJjoegdfxf6tubn55MmTISEhGhoaVMsbb7yRk5Mj\nFAqZDUbT19efOXPm9u3b7e3t5dvZlrysrOzRo0e+vr50y/jx4wkhBQUFbIv6+PHjGTNmTJ8+\nnW4xNTWVSqVSqZRtUWnLli0zMTGZN28e3cLaqMAGKvHxUJXJjaJCUxxRzVmOYKLrHRR2fxOL\nxVKp1MnJiW6hXotEIuZC/Q9bW9vly5cvX77cxsZGvp1tyY2MjIqKiv71r3/RLWfPniWEvPTS\nS2yLamlpefLkydGjR7e1td27dy89Pf3IkSNvvPGGmpoa26JS0tLSjh07duTIET6fTzeyMyqw\nhEp8PFRlcqOo0BRHVHCWI5joeg2F3d+oD8fAgQPpFuq1WCxmLFP3sC25pqbm8OHDdXV1qc3f\nf//9nXfecXZ29vPzY1tUWlpa2tChQ/38/CwtLamrZ1gYtby8PDw8fNeuXXZ2dvLtLIwK7KHS\nHw92hlfFKY6oyCxHMNH1BRR2f5PJZB22t7W1KTlJT7E2eXV19fvvvz9+/HgbG5v09HR1dXXW\nRp00adK5c+e+/PLL0tLS8ePHt7W1sTDq22+/PWrUqCVLlrRrZ2FUYA+V/niwPLwKTXFERWY5\ngomuL/CfPaR/MDU1JYRUVVXRLZWVlYQQMzMzxjJ1DzuT//rrr//6179aWlri4+MjIiKoM+rs\njEoIGThwoLe3t7e3t4WFxYwZM86cOfPSSy8RNkVNSEg4c+bMn3/+WVtbSwhpaWmRSqU1NTWa\nmpqs/akCG6j0x4PN4VVriiOqMMsRTHR9BGfs/mZubs7n8wsLC+kW6vWQIUOYC9UtLEz+yy+/\nzJgxY9SoUTdu3Hjvvffo6yTYFvWnn34aPXp0fX093TJs2DBCSFVVFduiXr9+vaGhwcnJydDQ\n0NDQ8MCBAyKRyNDQcMmSJWyLCqyi0h8P1oZXlSmOqNQsRzDR9RWmbsdloddee2306NHUs4hk\nMllQUBA7b6WeOXNmuycCsCq5RCKxsrKaOXOmVCp9updVUW/evEkISUlJoVu++OIL8v/auZuQ\nqPo9gONn4kKmvQhlhYsgghhS6IUoBsxVyzYJphSM2yLJoloMFUHRIriLFkFBQS+EoEbYooJo\nUSRusogga6NBYbTQjYwalJ67GPD21HPvE2rMOX8+n9V4zln8Dow/vjhnjKLBwcE4YaMODw8/\n/cHevXtramqePn36/v37pI1K0qTo7ZHw5VaSohUXp2rLxRbdAhF2/9XX17d48eJ8Pv/w4cOj\nR48uWrTox1+G5Ph19yVq8r6+viiK8vn8v//qzZs3SRs1juNcLlddXX3+/PnOzs5CoVBZWdna\n2jp7I4ka9Uft7e0//d/OxI5K2aXo7ZHw5TY7UopWXJzaLRdbdHMl7P7iyZMnjY2Ny5cv37lz\nZ1dXV7nH+Xu/7r44SZPfvHnzb/82fP369aSNGsfx2NjYkSNHtm7dWlVVVVdXd+HCha9fv86e\nTdSoP/pp38UJHpUkSMvbI+HLrSRdKy5O7ZaLLbq5ysT/45smAACkiy9PAAAEQtgBAARC2AEA\nBELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgB\nAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELY\nAQAEQtgBAARC2AEABELYkRTNzc3r168v9xQAf5Zdxx8l7AAAAiHsAAACIexIgampqdOnT2/a\ntGnJkiW1tbVtbW1jY2OlUzMzM+fOnctms2vWrGlraxsYGMhkMiMjI+UdGGAO7Drm71/lHgD+\n2eHDh7u6ujo6OjZv3jw4OHjp0qUoim7dulU6dePGjUKhkM1mu7u79+3bV+5hAebIrmP+hB0p\n8P3794sXL7a3t5d+HB0d7evri6JoaGjo2rVrly9fPnjwYBRFLS0tjY2NHz58KOesAHNl1zF/\nwo4UuH37dunFyMhIf3//o0ePqqqqoijq7++fnp4+cODA7JUtLS3Pnz8vz5QA82PXMX+esSMF\nXr9+vXv37pUrV+Zyue7u7tWrV5eOf/r0aenSpcuWLZu9cu3atWWaEWC+7DrmT9iRdJOTkw0N\nDTU1Nc+ePfv48WNPT09DQ0PpVG1tbbFYLBaLsxd/+fKlTGMCzItdx4IQdiTdq1evJiYmOjo6\n6uvrS0fevn1berFjx45MJnPnzp3Zi7u6usowIsC82XUsCM/YkSDFYrH0/a9ZK1asyOVyFRUV\nZ86cOXny5MzMzNWrV1+8eDEzMzMwMLB9+/Z8Pt/R0fH58+dsNtvT0zM6OhpFUSaTKdMdAPwz\nu44/JxPHcblngCiKoubm5rt37/50MJvNvnv37sGDB4VCYWhoaOPGjYcOHdq1a9eePXuqq6tf\nvnz57du3s2fPdnZ2Tk9P79+/f9u2ba2trRMTE5WVlWW5C4D/z67jjxJ2pNjU1NTw8PCGDRsq\nKipKR65cuXL8+PHJycnyDgawgOw6fp9n7EixiYmJ+vr6+/fvzx55/Pjxli1byjgSwIKz6/h9\nnrEjxVatWtXU1HTs2LHp6el169bdu3evt7f31884AFLNruP3+SiWdBsfHz916lRvb+/4+Hhd\nXd2JEyeamprKPRTAArPr+E3CDgAgEJ6xAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISw\nAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiE\nsAMACISwAwAIhLADAAiEsAMACMR/AKsNpis2F3LeAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “PACF of Y2”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Autocorrelation and partial autocorrelation plots\n",
    "par(mfrow=c(2, 2)) # set up the graphics  \n",
    "acf(j2, 48, main=\"ACF of Y2\") # autocorrelation function plot \n",
    "pacf(j2, 48, main=\"PACF of Y2\") # partial autocorrelation function \n",
    "acf(ts(j2, freq=1), 48, main=\"ACF of Y2\")        # autocorrelation function plot \n",
    "pacf(ts(j2, freq=1), 48, main=\"PACF of Y2\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "\n",
    "We must note that in the autocorrelation plots presented above, the **dashed lines** are the approximate **two standard error confidence bounds** computed by ± 1,96* (1 / T), where T is the number observations.\n",
    "\n",
    "If the autocorrelation is **within these bounds**, it is **not** significantly different from zero at (approximately) **5% level of significance**.\n",
    "\n",
    "So we conclude  that:\n",
    "\n",
    "* From **ACF plot** we observe that we have **autocorrelation for lag=1 and lag=2**. We must check a ** 2 lag moving average** model **(MA(2))**\n",
    "* From **PACF plot** we observe that we also have **partial autocorrelation for lag=1**. We must check an ** 1 lag autoregressive** model **(AR(1))**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on the stationary time series, i.e. the monthly returns.\n",
    "We can:\n",
    "* **test for normality of Y1**\n",
    "* **create the plot of the histogram together with a density plot**\n",
    "* **also create the normal QQplot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tShapiro-Wilk normality test\n",
       "\n",
       "data:  j2\n",
       "W = 0.88127, p-value = 2.648e-10\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1lVOFrydRyv0gzVpWhvSQ08/8L7UqRZLl12LfN6HP7JFy7fgfCA489J8InEntfO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\nrkJggg==",
      "text/plain": [
       "Plot with title “Normal QQplot of Y2”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shapiro test of normality\n",
    "shapiro.test(j2)\n",
    "\n",
    "par(mfrow=c(2,1))\n",
    "hist(j2, prob=TRUE, 50) # histogram\n",
    "\n",
    "lines(density(j2))\n",
    "# smooth it - ?density for details\n",
    "qqnorm(j2, main=\"Normal QQplot of Y2\") # normal Q-Q plot\n",
    "qqline(j2) # add a line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusions:\n",
    "* From Shapiro Wilk test (p-value = 2.648e-10) we **reject** the Null hypothesis of Normality.'\n",
    "* From the Histogram we see a left skewness.\n",
    "* From the QQplot we see several points off the qqline.\n",
    "\n",
    "So we conclude that we **don't have Normality**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation of ARMA models -  UNIT ROOT TESTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoregressive Models (AR(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can fit an **autoregressive time series model** to the Y1 data, by selecting the complexity of the\n",
    "model **based on AIC**. Then, we perform an **augmented Dickey-Fuller test** of unit root, based on a model\n",
    "with constant and trend "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "ar(x = j2)\n",
       "\n",
       "Coefficients:\n",
       "     1       2  \n",
       "0.2296  0.1243  \n",
       "\n",
       "Order selected 2  sigma^2 estimated as  0.0001043"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Order: 2'"
      ],
      "text/latex": [
       "'Order: 2'"
      ],
      "text/markdown": [
       "'Order: 2'"
      ],
      "text/plain": [
       "[1] \"Order: 2\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unit root testing for the Y1 series\n",
    "# Fits an autoregressive model, and selects the best AR order based on AIC\n",
    "m2 = ar(j2)\n",
    "m2\n",
    "paste('Order:', m2$order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking autoregressive model without drift and without trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # \n",
       "############################################################### \n",
       "\n",
       "The value of the test statistic is: -5.4669 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression none \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.058347 -0.001957  0.002613  0.007711  0.058248 \n",
       "\n",
       "Coefficients:\n",
       "           Estimate Std. Error t value Pr(>|t|)    \n",
       "z.lag.1    -0.42578    0.07788  -5.467 1.68e-07 ***\n",
       "z.diff.lag -0.24318    0.07465  -3.257  0.00137 ** \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.01061 on 164 degrees of freedom\n",
       "Multiple R-squared:  0.3281,\tAdjusted R-squared:  0.3199 \n",
       "F-statistic: 40.05 on 2 and 164 DF,  p-value: 6.87e-15\n",
       "\n",
       "\n",
       "Value of test-statistic is: -5.4669 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau1 -2.58 -1.95 -1.62\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m21 <- ur.df(j2, type='none', lags=m2$order-1)\n",
    "m21\n",
    "summary(m21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Critical Value (CV) for 5% is -1.95\n",
    "* The value of the test statistic for lag=1 without drift and without trend is: -5.4669\n",
    "* z.lag.1 and z.diff.lag are statistically significant\n",
    "\n",
    "Obviously, the null hypothesis of non-stationarity for the Y1 series is rejected. Therefore the returns of Y1 seem to be a stationary process.\n",
    "\n",
    "### Checking autoregressive model with drift (intercept) and without trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # \n",
       "############################################################### \n",
       "\n",
       "The value of the test statistic is: -7.0302 24.731 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.063758 -0.004284 -0.000036  0.005364  0.052803 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.0040331  0.0009866   4.088 6.82e-05 ***\n",
       "z.lag.1     -0.6560520  0.0933188  -7.030 5.37e-11 ***\n",
       "z.diff.lag  -0.1277698  0.0766979  -1.666   0.0977 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.01013 on 163 degrees of freedom\n",
       "Multiple R-squared:  0.3906,\tAdjusted R-squared:  0.3831 \n",
       "F-statistic: 52.23 on 2 and 163 DF,  p-value: < 2.2e-16\n",
       "\n",
       "\n",
       "Value of test-statistic is: -7.0302 24.731 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.46 -2.88 -2.57\n",
       "phi1  6.52  4.63  3.81\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unit root testing for the Y1 series\n",
    "# Fits an autoregressive model, and selects the best AR order based on AIC\n",
    "m22 <- ur.df(j2, type=\"drift\", lags=m2$order-1)\n",
    "m22\n",
    "summary(m22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Critical Value (CV) for 5% is -2.88\n",
    "* The value of the test statistic for lag=1 with drift is: -7.0302\n",
    "* Both Intercept and z.lag.1 are statistically significant\n",
    "* z.diff.lag is not statistically significant\n",
    "\n",
    "Obviously, the null hypothesis of non-stationarity for the Y1 series is rejected. Therefore the returns of Y1 seem to be a stationary process.\n",
    "\n",
    "### Checking autoregressive model with Drift (intercept) and Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # \n",
       "############################################################### \n",
       "\n",
       "The value of the test statistic is: -7.2329 17.4929 26.2202 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression trend \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.064083 -0.004363  0.000646  0.005553  0.050346 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  6.583e-03  1.875e-03   3.510  0.00058 ***\n",
       "z.lag.1     -6.972e-01  9.640e-02  -7.233 1.78e-11 ***\n",
       "tt          -2.706e-05  1.696e-05  -1.596  0.11243    \n",
       "z.diff.lag  -1.060e-01  7.755e-02  -1.366  0.17374    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.01009 on 162 degrees of freedom\n",
       "Multiple R-squared:    0.4,\tAdjusted R-squared:  0.3889 \n",
       "F-statistic:    36 on 3 and 162 DF,  p-value: < 2.2e-16\n",
       "\n",
       "\n",
       "Value of test-statistic is: -7.2329 17.4929 26.2202 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau3 -3.99 -3.43 -3.13\n",
       "phi2  6.22  4.75  4.07\n",
       "phi3  8.43  6.49  5.47\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m23 <- ur.df(j2, type=\"trend\", lags=m2$order-1)\n",
    "m23\n",
    "summary(m23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Critical Value (CV) for 5% is -3.43\n",
    "* The value of the test statistic for lag=1 with drift is: -7.2329\n",
    "* Both Intercept and z.lag.1 are statistically significant\n",
    "* Trend and z.diff.lag are **not** significant.\n",
    "\n",
    "Obviously, the null hypothesis of non-stationarity for the Y1 series is rejected. Therefore the returns of Y1 seem to be a stationary process.\n",
    "\n",
    "**Thus the model does not have a trend!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will check the autoregressive of lag=1 model (AR(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = j2, order = c(1, 0, 0))\n",
       "\n",
       "Coefficients:\n",
       "         ar1  intercept\n",
       "      0.2634     0.0065\n",
       "s.e.  0.0746     0.0011\n",
       "\n",
       "sigma^2 estimated as 0.000104:  log likelihood = 531.99,  aic = -1057.97"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar1fit <- arima(j2, order=c(1,0,0))\n",
    "ar1fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The estimated model (AR(1) can be written in the form:\n",
    "\n",
    "$$y_{t} = μ*(1-φ_{1}) + φ_{1} * y_{t-1} + e_{t}$$\n",
    "\n",
    "$$or$$ \n",
    "\n",
    "$$y_{t} = 0.0065*(1- 0.2634) + 0.2634 * y_{t-1} + e_{t}$$\n",
    "\n",
    "$$or$$ \n",
    "\n",
    "$$y_{t} = 0.0047879 + 0.2473 * y_{t-1} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moving Average Models MA(q)\n",
    "\n",
    "#### We check the moving average of lag=2 model (MA(2))\n",
    "\n",
    "Note: \n",
    "This model is not restricted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = j2, order = c(0, 0, 2))\n",
       "\n",
       "Coefficients:\n",
       "         ma1     ma2  intercept\n",
       "      0.2311  0.1724     0.0065\n",
       "s.e.  0.0758  0.0739     0.0011\n",
       "\n",
       "sigma^2 estimated as 0.0001025:  log likelihood = 533.18,  aic = -1058.36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma2fit <- arima(j2, order=c(0, 0, 2))\n",
    "ma2fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The estimated model MA(2) can be written in the form:**\n",
    "\n",
    "$$y_{t} - 0.0065 = 0.2311* e_{t-1} + 0.1724* e_{t-2} + e_{t}$$\n",
    "\n",
    "$$or$$ \n",
    "\n",
    "$$y_{t} = 0.0065 + 0.2311* e_{t-1} + 0.1724* e_{t-2} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets now fit an ARMA(1, 2) model. The command is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = j2, order = c(1, 0, 2))\n",
       "\n",
       "Coefficients:\n",
       "         ar1      ma1     ma2  intercept\n",
       "      0.3539  -0.1185  0.0942     0.0065\n",
       "s.e.  0.8266   0.8343  0.2301     0.0012\n",
       "\n",
       "sigma^2 estimated as 0.0001023:  log likelihood = 533.3,  aic = -1056.61"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arma12fit <- arima(j2, order=c(1, 0, 2))\n",
    "arma12fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The estimated ARMA(1, 2) model can be written in the form:\n",
    "\n",
    "\n",
    "$$y_{t} - μ = φ_{1}*(y_{t-1} -μ) + θ_{1} * e_{t-1} + θ_{2} * e_{t-2} + e_{t}$$\n",
    "\n",
    "$$or$$ \n",
    "\n",
    "$$y_{t} =μ +  φ_{1}*(y_{t-1} -μ) + θ_{1} * e_{t-1} + θ_{2} * e_{t-2} + e_{t}$$\n",
    "\n",
    "$$or$$ \n",
    "\n",
    "$$y_{t} = 0.0065 +  0.3539*(y_{t-1} -0.0065) - 0.1185 * e_{t-1} + 0.0942 * e_{t-2} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarizing we have the following:\n",
    "* AR(1): \n",
    "  * Variance: **0.0001040**\n",
    "  * AIC: **-1057.97**\n",
    "* MA(2):\n",
    "  * Variance: **0.0001025**\n",
    "  * AIC: **-1058.36**\n",
    "* ARMA(1, 2)\n",
    "  * Variance: **0.0001023**\n",
    "  * AIC: **-1056.61**\n",
    "\n",
    "  \n",
    "### Conclusion:\n",
    "\n",
    "* The model with the lowest variance and lower AIC score is the best. \n",
    "* Here, **MA(2)** has the smallest AIC with the second smallest variance. Thus, it's the model that best fits the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnostic plots\n",
    "\n",
    "Now we will provide some **diagnostic plots** for the **residuals** of the best model (MA(2)).\n",
    "Based on the residuals of the restricted MA(2) model, we will present the:\n",
    "* autocorrelation **(acf)** plots and the partial autocorrelation **(pacf)** plots of the **estimated residuals**\n",
    "* the autocorrelation plots and the partial autocorrelation plots of the **squared** residuals (check **heteroskedasticity**)\n",
    "* normality plots (examine the assumption of **normality of residuals**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P7kn7/ryspKW1vb+isWFBTIZDImcvfuXYVCwe6qq6qq7O3tlVa8dOnS06dP2Q26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yN2/ePKFQWFhYiPNzAKBIKOwAAORi6tSp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\niP8HckD9DiJemj8AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Normal QQplot of residuals”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma2residualsTS <- ts(ma2fit$residuals, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "\n",
    "par(mfrow=c(3,2))\n",
    "\n",
    "# set up the graphics\n",
    "acf(ts(ma2residualsTS,freq=1), 48, main=\"ACF of residuals\")\n",
    "pacf(ts(ma2residualsTS,freq=1), 48, main=\"PACF of residuals\")\n",
    "acf(ts(ma2residualsTS^2,freq=1), 48, main=\"ACF of squared residuals\")\n",
    "pacf(ts(ma2residualsTS^2,freq=1), 48, main=\"PACF of squared residuals\")\n",
    "qqnorm(ma2residualsTS,main=\"Normal QQplot of residuals\")\n",
    "qqline(ma2residualsTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the residual plots presented above:\n",
    "\n",
    "* From the ACF and PACF of the residuals, it seems that the assumptions with respect to them are satisfied\n",
    "* From the PACF fo the squared residuals it seems that there we have homoskedasticity\n",
    "* From the Normal  QQPlot of residuals we see that we may have normality issues for the residuals of the MA(2) model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RieAAAAkBkEu8TCt2IxPAEAACA/CHaJ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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast <- predict(ma2fit, 12)\n",
    "\n",
    "# plot of forecasts with 1 standard error\n",
    "par(mfrow=c(1,1))\n",
    "UL <- forecast$pred + forecast$se\n",
    "LL <- forecast$pred - forecast$se\n",
    "\n",
    "predTS <- ts(forecast$pred, frequency=12, start = c(2004, 12))\n",
    "UL_TS <- ts(forecast$pred, frequency=12, start = c(2004, 12))\n",
    "LL_TS <- ts(forecast$pred, frequency=12, start = c(2004, 12))\n",
    "\n",
    "min_x <- min(ma2residualsTS, LL)\n",
    "max_x <- max(ma2residualsTS, UL)\n",
    "\n",
    "ts.plot(ma2residualsTS, predTS)\n",
    "lines(predTS, col=\"red\", type=\"o\")\n",
    "lines(UL_TS, col=\"blue\", lty=\"dashed\")\n",
    "lines(LL_TS, col=\"blue\", lty=\"dashed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.60661500106422e-05"
      ],
      "text/latex": [
       "1.60661500106422e-05"
      ],
      "text/markdown": [
       "1.60661500106422e-05"
      ],
      "text/plain": [
       "[1] 1.606615e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(hydroGOF)\n",
    "MSE <- mse(forecast$pred, forecast$se)\n",
    "MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$MSE = 1.606 * 10^{-5}$$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R [conda env:mro_env]",
   "language": "R",
   "name": "conda-env-mro_env-r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}