ipynb/timeseries.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# The Amsterdam Resources for doing Computational Social Science\n",
    "## Time series analysis with Python\n",
    "\n",
    "(c) Joanna Strycharz, Damian Trilling, Nadine Strauß\n",
    "\n",
    "Thank you for using our resources. If this template was useful for your analysis, please cite our paper on which this tutorial is based: Strycharz, J., Strauss, N., & Trilling, D. (2018). The role of media coverage in explaining stock market fluctuations: Insights for strategic financial communication. International Journal of Strategic Communication, 12(1), 67–85. doi:10.1080/1553118X.2017.1378220"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are we going to do?\n",
    "\n",
    "In this tutorial, you learn how to estimate Vector Autoregression (VAR) models as described by Vliegenthart (2014). VAR analyses are appropriate when assessing relationships among variables in which the direction, duration or delay of effects is not clearly motivated by theory. VAR analyses consider both variables as endogenous.\n",
    "This can be useful to study temporal dynamics of newspaper coverage. A typical workflow might include the use of regular expressions to select relevant articles and sentiment analysis, supervised machine learning, or topic modeling, to enrich the dataset. For this tutorial, we prepared a simple example dataset that contains basic information on media coverage about a company, and a second dataset that includes stock prices of that company.\n",
    "\n",
    "The following steps will be undertaken in the tutorial to conduct the analysis: data will be loaded, adjusted and merged. Next, it will be aggreagted at a daily level. Finally, stationarity tests and possibly differencing of the time series will follow and subsequently, the VAR model will be constructed. \n",
    "\n",
    "The example we use is loosely based on an analysis presented in Strycharz, Strauß, & Trilling (2016).\n",
    "\n",
    "\n",
    "## What are we going to use?\n",
    "\n",
    "We will use the Python module pandas as a general framework for data aggregation and data analysis. For the VAR models, we are going to combine pandas with statsmodels. Before you start, make sure you have the most recent version of both. Most likely, you want to run something like this:\n",
    "\n",
    "    pip3 install pandas --upgrade\n",
    "    pip3 install numpy --upgrade\n",
    "    pip3 install statsmodels --upgrade\n",
    "\n",
    "If you want to learn more about how to use and install Python, you can read our general manual which is provided [here](https://github.com/damian0604/bdaca/blob/master/book/bd-aca_book.pdf). An alternative (probably quicker) solution is to install [Anaconda](https://www.continuum.io/downloads).\n",
    "\n",
    "To learn more about data analysis using pandas, we recommend the book by McKinney (2012).\n",
    "\n",
    "We'll also use an example dataset, which you can download here:\n",
    "- [media.csv](media.csv)\n",
    "- [stock.csv](stock.csv)\n",
    "\n",
    "\n",
    "## References\n",
    "McKinney, W. (2012). Python for data analysis. Sebastopol, CA: O’Reilly. ([free download](http://it-ebooks.info/book/1041/))\n",
    "\n",
    "Strycharz, J., Strauß, N., & Trilling, D. (2016). Media coverage and share price volatility: Is it only attention that matters? *Manuscript submitted for presentation*\n",
    "\n",
    "Vliegenthart, R. (2014). Moving up. Applying aggregate level time series analysis in the study of media coverage. *Quality & Quantity, 48*(5), 2427-2445.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's get started!\n",
    "\n",
    "First, we are going to import all neccessary modules. Also, we want to make sure that all plots we will make appear within this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.tsa as smtsa\n",
    "from pandas.tseries.offsets import BDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Loading, inspecting, and merging datasets\n",
    "\n",
    "### Loading\n",
    "We have two datasets: One with media data and one with stock exchange data. We will start by merging the two.\n",
    "So, let's load our first dataset in a dataframe and inspect it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'length', 'positivity', 'negativity', 'pubdate_day',\n",
      "       'pubdate_month', 'pubdate_year', 'pubdate_dayofweek', 'pubdate_weeknr'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "media = pd.read_csv('media.csv')\n",
    "print(media.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Inspecting\n",
    "Let's also have a look at the table. Each row represents one article about company X. For each article, we have a unique ID, the length of the article, two measures of sentiment (positivity and negativity), and the date on which it was published."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
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   "outputs": [
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       "      <th>964</th>\n",
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       "      <td>3</td>\n",
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       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
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       "      <th>965</th>\n",
       "      <td>57250bdb2d00597c054637a8</td>\n",
       "      <td>2067</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>57250c232d00597c05463846</td>\n",
       "      <td>8667</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>57250c2b2d00597c05463855</td>\n",
       "      <td>1611</td>\n",
       "      <td>5</td>\n",
       "      <td>-3</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>57250cca2d00597c05463968</td>\n",
       "      <td>713</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>57250cce2d00597c05463974</td>\n",
       "      <td>1352</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>57250cdd2d00597c0546398f</td>\n",
       "      <td>2807</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>57250dbb2d00597c05463b17</td>\n",
       "      <td>3118</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>57250e262d00597c05463bfb</td>\n",
       "      <td>2784</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>57250e3d2d00597c05463c24</td>\n",
       "      <td>8485</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>57250e4d2d00597c05463c41</td>\n",
       "      <td>1146</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>57250e772d00597c05463ca2</td>\n",
       "      <td>1825</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>57250ec42d00597c05463d53</td>\n",
       "      <td>9699</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>57250ec82d00597c05463d5d</td>\n",
       "      <td>9699</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>57250ecf2d00597c05463d6d</td>\n",
       "      <td>1184</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>57250f542d00597c05463e47</td>\n",
       "      <td>5037</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>57250fd82d00597c05463f39</td>\n",
       "      <td>2606</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>572510ae2d00597c054640cd</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>572510ae2d00597c054640cf</td>\n",
       "      <td>1282</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>572510af2d00597c054640d0</td>\n",
       "      <td>1282</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>572510cf2d00597c05464117</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>572511602d00597c05464232</td>\n",
       "      <td>2044</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>572511ab2d00597c054642c7</td>\n",
       "      <td>2718</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>572511ad2d00597c054642c9</td>\n",
       "      <td>809</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>572511ae2d00597c054642cd</td>\n",
       "      <td>4025</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>572511b32d00597c054642d8</td>\n",
       "      <td>941</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>572511cc2d00597c05464309</td>\n",
       "      <td>5697</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>572512fc2d00597c054644c4</td>\n",
       "      <td>4451</td>\n",
       "      <td>5</td>\n",
       "      <td>-3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>992 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  length  positivity  negativity  pubdate_day  \\\n",
       "0    569eba862d00594dd4c62efb    3261           2          -3            7   \n",
       "1    569ebbbd2d00594dd4c6311b    1980           1          -3           10   \n",
       "2    569ebc102d00594dd4c631a5    4068           2          -3           11   \n",
       "3    569ebc852d00594dd4c6326c     720           1          -2           13   \n",
       "4    569ebdf92d00594dd4c634ea    2278           2          -3           20   \n",
       "5    569ebf392d00594dd4c63715    3956           3          -4           23   \n",
       "6    569ec0882d00594dd4c639e5     670           3          -2           27   \n",
       "7    569ec1082d00594dd4c63af5    2023           3          -3           29   \n",
       "8    569ec14c2d00594dd4c63b73     812           1          -4           30   \n",
       "9    569ec1812d00594dd4c63bd4     887           1          -2           30   \n",
       "10   569f5cbd2d0059663a7bca00    2281           3          -3           17   \n",
       "11   569f5cbd2d0059663a7bca65    2240           1          -3           20   \n",
       "12   569f5cbd2d0059663a7bcac4    2149           3          -3           30   \n",
       "13   569f5cc42d0059663a7bd4a2     568           1          -1            7   \n",
       "14   569f5cc52d0059663a7bd53c     773           3          -2            8   \n",
       "15   569f5cc62d0059663a7bd6c3    3590           3          -4            9   \n",
       "16   569f5cc62d0059663a7bd6e7     364           1          -3            9   \n",
       "17   569f5cc72d0059663a7bda75    1964           3          -2           13   \n",
       "18   569f5cc82d0059663a7bdbc0    3102           3          -3           14   \n",
       "19   569f5cc82d0059663a7bdbce     840           1          -2           14   \n",
       "20   569f5cc82d0059663a7bdc05    3837           4          -3           14   \n",
       "21   569f5cc82d0059663a7bdc69    1604           1          -3           14   \n",
       "22   569f5cc92d0059663a7bdf11    1277           1          -2           16   \n",
       "23   569f5cca2d0059663a7bdfe4    1487           3          -3           17   \n",
       "24   569f5cca2d0059663a7bdfeb    1210           3          -3           17   \n",
       "25   569f5cca2d0059663a7bdfef    3123           3          -3           17   \n",
       "26   569f5cca2d0059663a7bdff7    1250           3          -2           17   \n",
       "27   569f5cca2d0059663a7be01f    1644           3          -3           17   \n",
       "28   569f5cca2d0059663a7be05a     776           2          -2           17   \n",
       "29   569f5cca2d0059663a7be0a5    5223           4          -3           17   \n",
       "..                        ...     ...         ...         ...          ...   \n",
       "962  57250a892d00597c054634e1    2618           3          -3           15   \n",
       "963  57250a8d2d00597c054634eb    5154           4          -3           15   \n",
       "964  57250ba12d00597c0546373a    5183           3          -3           17   \n",
       "965  57250bdb2d00597c054637a8    2067           3          -3           17   \n",
       "966  57250c232d00597c05463846    8667           3          -3           18   \n",
       "967  57250c2b2d00597c05463855    1611           5          -3           18   \n",
       "968  57250cca2d00597c05463968     713           1          -2           19   \n",
       "969  57250cce2d00597c05463974    1352           3          -2           19   \n",
       "970  57250cdd2d00597c0546398f    2807           3          -3           19   \n",
       "971  57250dbb2d00597c05463b17    3118           3          -3           21   \n",
       "972  57250e262d00597c05463bfb    2784           4          -3           22   \n",
       "973  57250e3d2d00597c05463c24    8485           3          -4           22   \n",
       "974  57250e4d2d00597c05463c41    1146           4          -3           22   \n",
       "975  57250e772d00597c05463ca2    1825           3          -2           22   \n",
       "976  57250ec42d00597c05463d53    9699           3          -4           23   \n",
       "977  57250ec82d00597c05463d5d    9699           3          -4           23   \n",
       "978  57250ecf2d00597c05463d6d    1184           3          -3           23   \n",
       "979  57250f542d00597c05463e47    5037           4          -3           24   \n",
       "980  57250fd82d00597c05463f39    2606           3          -3           25   \n",
       "981  572510ae2d00597c054640cd     331           1          -3           28   \n",
       "982  572510ae2d00597c054640cf    1282           2          -2           28   \n",
       "983  572510af2d00597c054640d0    1282           2          -2           28   \n",
       "984  572510cf2d00597c05464117     890           1          -3           28   \n",
       "985  572511602d00597c05464232    2044           3          -3           29   \n",
       "986  572511ab2d00597c054642c7    2718           3          -3           30   \n",
       "987  572511ad2d00597c054642c9     809           1          -2           30   \n",
       "988  572511ae2d00597c054642cd    4025           4          -3           30   \n",
       "989  572511b32d00597c054642d8     941           1          -4           30   \n",
       "990  572511cc2d00597c05464309    5697           3          -3           30   \n",
       "991  572512fc2d00597c054644c4    4451           5          -3            1   \n",
       "\n",
       "     pubdate_month  pubdate_year  pubdate_dayofweek  pubdate_weeknr  \n",
       "0                1          2015                  2               1  \n",
       "1                1          2015                  5               1  \n",
       "2                1          2015                  6               2  \n",
       "3                1          2015                  1               2  \n",
       "4                1          2015                  1               3  \n",
       "5                1          2015                  4               3  \n",
       "6                1          2015                  1               4  \n",
       "7                1          2015                  3               4  \n",
       "8                1          2015                  4               4  \n",
       "9                1          2015                  4               4  \n",
       "10               1          2014                  4               2  \n",
       "11               1          2014                  0               3  \n",
       "12               1          2014                  3               4  \n",
       "13               1          2014                  1               1  \n",
       "14               1          2014                  2               1  \n",
       "15               1          2014                  3               1  \n",
       "16               1          2014                  3               1  \n",
       "17               1          2014                  0               2  \n",
       "18               1          2014                  1               2  \n",
       "19               1          2014                  1               2  \n",
       "20               1          2014                  1               2  \n",
       "21               1          2014                  1               2  \n",
       "22               1          2014                  3               2  \n",
       "23               1          2014                  4               2  \n",
       "24               1          2014                  4               2  \n",
       "25               1          2014                  4               2  \n",
       "26               1          2014                  4               2  \n",
       "27               1          2014                  4               2  \n",
       "28               1          2014                  4               2  \n",
       "29               1          2014                  4               2  \n",
       "..             ...           ...                ...             ...  \n",
       "962             12          2015                  1              50  \n",
       "963             12          2015                  1              50  \n",
       "964             12          2015                  3              50  \n",
       "965             12          2015                  3              50  \n",
       "966             12          2015                  4              50  \n",
       "967             12          2015                  4              50  \n",
       "968             12          2015                  5              50  \n",
       "969             12          2015                  5              50  \n",
       "970             12          2015                  5              50  \n",
       "971             12          2015                  0              51  \n",
       "972             12          2015                  1              51  \n",
       "973             12          2015                  1              51  \n",
       "974             12          2015                  1              51  \n",
       "975             12          2015                  1              51  \n",
       "976             12          2015                  2              51  \n",
       "977             12          2015                  2              51  \n",
       "978             12          2015                  2              51  \n",
       "979             12          2015                  3              51  \n",
       "980             12          2015                  4              51  \n",
       "981             12          2015                  0              52  \n",
       "982             12          2015                  0              52  \n",
       "983             12          2015                  0              52  \n",
       "984             12          2015                  0              52  \n",
       "985             12          2015                  1              52  \n",
       "986             12          2015                  2              52  \n",
       "987             12          2015                  2              52  \n",
       "988             12          2015                  2              52  \n",
       "989             12          2015                  2              52  \n",
       "990             12          2015                  2              52  \n",
       "991             12          2015                  1              48  \n",
       "\n",
       "[992 rows x 9 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get a even better overview of the dataset by using the methods .value_counts (to get a frequency table) or .describe (to get descriptive statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    418\n",
       "2015    574\n",
       "Name: pubdate_year, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media['pubdate_year'].value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>positivity</th>\n",
       "      <th>negativity</th>\n",
       "      <th>pubdate_day</th>\n",
       "      <th>pubdate_month</th>\n",
       "      <th>pubdate_year</th>\n",
       "      <th>pubdate_dayofweek</th>\n",
       "      <th>pubdate_weeknr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2652.804435</td>\n",
       "      <td>2.304435</td>\n",
       "      <td>-2.789315</td>\n",
       "      <td>15.268145</td>\n",
       "      <td>6.568548</td>\n",
       "      <td>2014.578629</td>\n",
       "      <td>2.405242</td>\n",
       "      <td>26.181452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2408.886191</td>\n",
       "      <td>1.042033</td>\n",
       "      <td>0.797933</td>\n",
       "      <td>8.983373</td>\n",
       "      <td>3.529512</td>\n",
       "      <td>0.494028</td>\n",
       "      <td>1.625072</td>\n",
       "      <td>15.374614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>104.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>906.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1968.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3686.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>22.250000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19912.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             length  positivity  negativity  pubdate_day  pubdate_month  \\\n",
       "count    992.000000  992.000000  992.000000   992.000000     992.000000   \n",
       "mean    2652.804435    2.304435   -2.789315    15.268145       6.568548   \n",
       "std     2408.886191    1.042033    0.797933     8.983373       3.529512   \n",
       "min      104.000000    1.000000   -5.000000     1.000000       1.000000   \n",
       "25%      906.000000    1.000000   -3.000000     7.000000       4.000000   \n",
       "50%     1968.000000    2.000000   -3.000000    15.000000       6.000000   \n",
       "75%     3686.000000    3.000000   -2.000000    22.250000      10.000000   \n",
       "max    19912.000000    5.000000   -1.000000    31.000000      12.000000   \n",
       "\n",
       "       pubdate_year  pubdate_dayofweek  pubdate_weeknr  \n",
       "count    992.000000         992.000000      992.000000  \n",
       "mean    2014.578629           2.405242       26.181452  \n",
       "std        0.494028           1.625072       15.374614  \n",
       "min     2014.000000           0.000000        1.000000  \n",
       "25%     2014.000000           1.000000       14.000000  \n",
       "50%     2015.000000           2.000000       24.000000  \n",
       "75%     2015.000000           4.000000       41.000000  \n",
       "max     2015.000000           6.000000       52.000000  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with dates\n",
    "\n",
    "We saw that our dataset contained date information in different columns: one for month, day, and year of publication, respectively. However, that's not what we want. In order to work with our data, we need to\n",
    "- get it into *one* column, formated as a datetime object, so that pandas understands the temporal order\n",
    "- change the days of all Saturdays and Sundays to the next Monday (because stock exchanges are closed on the weekend)\n",
    "- drop the unneccessary old date columns from the dataframe.\n",
    "\n",
    "Most importantly, this enables us to use the new datetime object as an *index*: This means that pandas does not address each row by its row number, but by its date. Note that this has nothing to do with aggregating yet, there can be several rows with the same date. Using a datetime index makes it not only very easy to merge our dataset with other datasets that are indexed by date as well, but it also makes it possible to automatically make plots and analysis over time.\n",
    "\n",
    "Luckily, all of this is very easy in pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We rename our columns, so that the to_datetime function which we use next recognizes which column contains what.\n",
    "media.rename(columns={'pubdate_year':'year','pubdate_month':'month','pubdate_day':'day'},inplace=True)\n",
    "\n",
    "# make new column called 'date' which contains a datetime object\n",
    "media['date']=pd.to_datetime(media[['day','month','year']])\n",
    "# convert to business days: Merge Saturday and Sunday with Monday\n",
    "media['date']=media['date']+ BDay(0)\n",
    "# drop old stuff\n",
    "media=media.drop(['day','month','year','pubdate_dayofweek','pubdate_weeknr'],axis=1)\n",
    "\n",
    "# index dataset by date (instead of by row number)\n",
    "media.index=pd.DatetimeIndex(media['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's always a good idea to check whether everything looks like expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>569f5cbd2d0059663a7bca00</td>\n",
       "      <td>2281</td>\n",
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       "      <th>2014-01-20</th>\n",
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       "      <th>2014-01-14</th>\n",
       "      <td>569f5cc82d0059663a7bdc69</td>\n",
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       "      <td>1</td>\n",
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       "      <td>569f5cc92d0059663a7bdf11</td>\n",
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       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>2014-01-16</td>\n",
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       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7bdfe4</td>\n",
       "      <td>1487</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2014-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7bdfeb</td>\n",
       "      <td>1210</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2014-01-17</td>\n",
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       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7bdfef</td>\n",
       "      <td>3123</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2014-01-17</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7bdff7</td>\n",
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       "      <td>3</td>\n",
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       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7be01f</td>\n",
       "      <td>1644</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7be05a</td>\n",
       "      <td>776</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>2014-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-17</th>\n",
       "      <td>569f5cca2d0059663a7be0a5</td>\n",
       "      <td>5223</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>2014-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>57250a892d00597c054634e1</td>\n",
       "      <td>2618</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>57250a8d2d00597c054634eb</td>\n",
       "      <td>5154</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>57250ba12d00597c0546373a</td>\n",
       "      <td>5183</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>57250bdb2d00597c054637a8</td>\n",
       "      <td>2067</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>57250c232d00597c05463846</td>\n",
       "      <td>8667</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>57250c2b2d00597c05463855</td>\n",
       "      <td>1611</td>\n",
       "      <td>5</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>57250cca2d00597c05463968</td>\n",
       "      <td>713</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-12-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>57250cce2d00597c05463974</td>\n",
       "      <td>1352</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-12-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>57250cdd2d00597c0546398f</td>\n",
       "      <td>2807</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>57250dbb2d00597c05463b17</td>\n",
       "      <td>3118</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>57250e262d00597c05463bfb</td>\n",
       "      <td>2784</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>57250e3d2d00597c05463c24</td>\n",
       "      <td>8485</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>2015-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>57250e4d2d00597c05463c41</td>\n",
       "      <td>1146</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>57250e772d00597c05463ca2</td>\n",
       "      <td>1825</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>57250ec42d00597c05463d53</td>\n",
       "      <td>9699</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>2015-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>57250ec82d00597c05463d5d</td>\n",
       "      <td>9699</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "      <td>2015-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>57250ecf2d00597c05463d6d</td>\n",
       "      <td>1184</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>57250f542d00597c05463e47</td>\n",
       "      <td>5037</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-25</th>\n",
       "      <td>57250fd82d00597c05463f39</td>\n",
       "      <td>2606</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>572510ae2d00597c054640cd</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>572510ae2d00597c054640cf</td>\n",
       "      <td>1282</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>572510af2d00597c054640d0</td>\n",
       "      <td>1282</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>572510cf2d00597c05464117</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>572511602d00597c05464232</td>\n",
       "      <td>2044</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>572511ab2d00597c054642c7</td>\n",
       "      <td>2718</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>572511ad2d00597c054642c9</td>\n",
       "      <td>809</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>572511ae2d00597c054642cd</td>\n",
       "      <td>4025</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>572511b32d00597c054642d8</td>\n",
       "      <td>941</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>2015-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>572511cc2d00597c05464309</td>\n",
       "      <td>5697</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>572512fc2d00597c054644c4</td>\n",
       "      <td>4451</td>\n",
       "      <td>5</td>\n",
       "      <td>-3</td>\n",
       "      <td>2015-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>992 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  id  length  positivity  negativity  \\\n",
       "date                                                                   \n",
       "2015-01-07  569eba862d00594dd4c62efb    3261           2          -3   \n",
       "2015-01-12  569ebbbd2d00594dd4c6311b    1980           1          -3   \n",
       "2015-01-12  569ebc102d00594dd4c631a5    4068           2          -3   \n",
       "2015-01-13  569ebc852d00594dd4c6326c     720           1          -2   \n",
       "2015-01-20  569ebdf92d00594dd4c634ea    2278           2          -3   \n",
       "2015-01-23  569ebf392d00594dd4c63715    3956           3          -4   \n",
       "2015-01-27  569ec0882d00594dd4c639e5     670           3          -2   \n",
       "2015-01-29  569ec1082d00594dd4c63af5    2023           3          -3   \n",
       "2015-01-30  569ec14c2d00594dd4c63b73     812           1          -4   \n",
       "2015-01-30  569ec1812d00594dd4c63bd4     887           1          -2   \n",
       "2014-01-17  569f5cbd2d0059663a7bca00    2281           3          -3   \n",
       "2014-01-20  569f5cbd2d0059663a7bca65    2240           1          -3   \n",
       "2014-01-30  569f5cbd2d0059663a7bcac4    2149           3          -3   \n",
       "2014-01-07  569f5cc42d0059663a7bd4a2     568           1          -1   \n",
       "2014-01-08  569f5cc52d0059663a7bd53c     773           3          -2   \n",
       "2014-01-09  569f5cc62d0059663a7bd6c3    3590           3          -4   \n",
       "2014-01-09  569f5cc62d0059663a7bd6e7     364           1          -3   \n",
       "2014-01-13  569f5cc72d0059663a7bda75    1964           3          -2   \n",
       "2014-01-14  569f5cc82d0059663a7bdbc0    3102           3          -3   \n",
       "2014-01-14  569f5cc82d0059663a7bdbce     840           1          -2   \n",
       "2014-01-14  569f5cc82d0059663a7bdc05    3837           4          -3   \n",
       "2014-01-14  569f5cc82d0059663a7bdc69    1604           1          -3   \n",
       "2014-01-16  569f5cc92d0059663a7bdf11    1277           1          -2   \n",
       "2014-01-17  569f5cca2d0059663a7bdfe4    1487           3          -3   \n",
       "2014-01-17  569f5cca2d0059663a7bdfeb    1210           3          -3   \n",
       "2014-01-17  569f5cca2d0059663a7bdfef    3123           3          -3   \n",
       "2014-01-17  569f5cca2d0059663a7bdff7    1250           3          -2   \n",
       "2014-01-17  569f5cca2d0059663a7be01f    1644           3          -3   \n",
       "2014-01-17  569f5cca2d0059663a7be05a     776           2          -2   \n",
       "2014-01-17  569f5cca2d0059663a7be0a5    5223           4          -3   \n",
       "...                              ...     ...         ...         ...   \n",
       "2015-12-15  57250a892d00597c054634e1    2618           3          -3   \n",
       "2015-12-15  57250a8d2d00597c054634eb    5154           4          -3   \n",
       "2015-12-17  57250ba12d00597c0546373a    5183           3          -3   \n",
       "2015-12-17  57250bdb2d00597c054637a8    2067           3          -3   \n",
       "2015-12-18  57250c232d00597c05463846    8667           3          -3   \n",
       "2015-12-18  57250c2b2d00597c05463855    1611           5          -3   \n",
       "2015-12-21  57250cca2d00597c05463968     713           1          -2   \n",
       "2015-12-21  57250cce2d00597c05463974    1352           3          -2   \n",
       "2015-12-21  57250cdd2d00597c0546398f    2807           3          -3   \n",
       "2015-12-21  57250dbb2d00597c05463b17    3118           3          -3   \n",
       "2015-12-22  57250e262d00597c05463bfb    2784           4          -3   \n",
       "2015-12-22  57250e3d2d00597c05463c24    8485           3          -4   \n",
       "2015-12-22  57250e4d2d00597c05463c41    1146           4          -3   \n",
       "2015-12-22  57250e772d00597c05463ca2    1825           3          -2   \n",
       "2015-12-23  57250ec42d00597c05463d53    9699           3          -4   \n",
       "2015-12-23  57250ec82d00597c05463d5d    9699           3          -4   \n",
       "2015-12-23  57250ecf2d00597c05463d6d    1184           3          -3   \n",
       "2015-12-24  57250f542d00597c05463e47    5037           4          -3   \n",
       "2015-12-25  57250fd82d00597c05463f39    2606           3          -3   \n",
       "2015-12-28  572510ae2d00597c054640cd     331           1          -3   \n",
       "2015-12-28  572510ae2d00597c054640cf    1282           2          -2   \n",
       "2015-12-28  572510af2d00597c054640d0    1282           2          -2   \n",
       "2015-12-28  572510cf2d00597c05464117     890           1          -3   \n",
       "2015-12-29  572511602d00597c05464232    2044           3          -3   \n",
       "2015-12-30  572511ab2d00597c054642c7    2718           3          -3   \n",
       "2015-12-30  572511ad2d00597c054642c9     809           1          -2   \n",
       "2015-12-30  572511ae2d00597c054642cd    4025           4          -3   \n",
       "2015-12-30  572511b32d00597c054642d8     941           1          -4   \n",
       "2015-12-30  572511cc2d00597c05464309    5697           3          -3   \n",
       "2015-12-01  572512fc2d00597c054644c4    4451           5          -3   \n",
       "\n",
       "                 date  \n",
       "date                   \n",
       "2015-01-07 2015-01-07  \n",
       "2015-01-12 2015-01-12  \n",
       "2015-01-12 2015-01-12  \n",
       "2015-01-13 2015-01-13  \n",
       "2015-01-20 2015-01-20  \n",
       "2015-01-23 2015-01-23  \n",
       "2015-01-27 2015-01-27  \n",
       "2015-01-29 2015-01-29  \n",
       "2015-01-30 2015-01-30  \n",
       "2015-01-30 2015-01-30  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-20 2014-01-20  \n",
       "2014-01-30 2014-01-30  \n",
       "2014-01-07 2014-01-07  \n",
       "2014-01-08 2014-01-08  \n",
       "2014-01-09 2014-01-09  \n",
       "2014-01-09 2014-01-09  \n",
       "2014-01-13 2014-01-13  \n",
       "2014-01-14 2014-01-14  \n",
       "2014-01-14 2014-01-14  \n",
       "2014-01-14 2014-01-14  \n",
       "2014-01-14 2014-01-14  \n",
       "2014-01-16 2014-01-16  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-17 2014-01-17  \n",
       "2014-01-17 2014-01-17  \n",
       "...               ...  \n",
       "2015-12-15 2015-12-15  \n",
       "2015-12-15 2015-12-15  \n",
       "2015-12-17 2015-12-17  \n",
       "2015-12-17 2015-12-17  \n",
       "2015-12-18 2015-12-18  \n",
       "2015-12-18 2015-12-18  \n",
       "2015-12-21 2015-12-21  \n",
       "2015-12-21 2015-12-21  \n",
       "2015-12-21 2015-12-21  \n",
       "2015-12-21 2015-12-21  \n",
       "2015-12-22 2015-12-22  \n",
       "2015-12-22 2015-12-22  \n",
       "2015-12-22 2015-12-22  \n",
       "2015-12-22 2015-12-22  \n",
       "2015-12-23 2015-12-23  \n",
       "2015-12-23 2015-12-23  \n",
       "2015-12-23 2015-12-23  \n",
       "2015-12-24 2015-12-24  \n",
       "2015-12-25 2015-12-25  \n",
       "2015-12-28 2015-12-28  \n",
       "2015-12-28 2015-12-28  \n",
       "2015-12-28 2015-12-28  \n",
       "2015-12-28 2015-12-28  \n",
       "2015-12-29 2015-12-29  \n",
       "2015-12-30 2015-12-30  \n",
       "2015-12-30 2015-12-30  \n",
       "2015-12-30 2015-12-30  \n",
       "2015-12-30 2015-12-30  \n",
       "2015-12-30 2015-12-30  \n",
       "2015-12-01 2015-12-01  \n",
       "\n",
       "[992 rows x 5 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating\n",
    "We want to aggregate per day. After all, our unit of analysis will become the day, not the article. We will use a .sum() function for that, basically adding up everything that happens on the same day. If there are three article on a given day, one of length 420, one of length 550, and one of length 530, then the aggretgated length for that day will be 420 + 550 + 530 = 1500. The same holds true for positvity and negativity.  \n",
    "However, for our first analysis, we simply want to know how many articles were published, so we'll just create a variable that is 1 for every case. After aggregation, our new variable \"visibility\" will then be 1 + 1 + 1 = 3 for that day.\n",
    "Of course, for other analyses, we could use other aggregation functions, for example .mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# make new variable to be able to count articles\n",
    "media['visibility']=1\n",
    "\n",
    "# aggregate\n",
    "media_day=media.groupby(by=['date']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if all of that makes sense and let's make some plots over time. As explained above, because our dataframe is indexed by date, we don't have to take care of that any more and can just plot the columns of the dataset we are interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2d9b90470>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ty8kxKjXecOoUrT/Bf0uW6KM2IagM2DgUFxPpKymhztSMhATdYHBd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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d99bfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_day[['positivity','negativity']].plot() # plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2d9af2ef0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WOwXxqfWQPld4f/e70uxv40Z3CWg/wVeH76UWHH6SrymvhMfI6OzDDwcLfn9/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\n4atyCf5+++U6/A0bcvO5/F1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IyxnSX7uWXidNAo48ktqEs1nvOTQ00P2MK/jyOvsRJfwa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\nIAiCBiA6/CAIgiBoAP4fnluXotp2tfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d999b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_day[['visibility']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge with stock dataset\n",
    "\n",
    "Now that we have prepared everything, we can open our stock dataset, make sure that it is also indexed by day (luckily, its date is already saved in a format that can automatically be transformed to a datetime object, so we don't have to mess around with different variables here)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stock=pd.read_csv('stock.csv',sep=',')\n",
    "stock['date']=pd.to_datetime(stock['Date'])\n",
    "stock.index=pd.DatetimeIndex(stock['date'])\n",
    "stock=stock.drop(['Date','date'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, it's always a goot idea to check whether the dataset looks like expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2d9b1f588>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9m+7du/P444/Ttm1bkpKSCA0NZefOnbeECJEOiKRGSMlJYdCPg4hOiy7X9m/s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d9955908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock[['Adj Close','Close']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can merge the two dataset with the .join() method. The .fillna(0) method replaces missing values with 0, which makes sense in our case because if on a given day there is no information on media coverage (no negativity, no positivity, etc.), then the reason is not that we do not know these attributes, but simply that there was no article published, which should be reflected in the data as 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mediastock = stock.join(media_day,how='left').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>length</th>\n",
       "      <th>positivity</th>\n",
       "      <th>negativity</th>\n",
       "      <th>visibility</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>21.095</td>\n",
       "      <td>21.095</td>\n",
       "      <td>21.095</td>\n",
       "      <td>21.095</td>\n",
       "      <td>0</td>\n",
       "      <td>20.6742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>21.135</td>\n",
       "      <td>21.140</td>\n",
       "      <td>20.915</td>\n",
       "      <td>21.095</td>\n",
       "      <td>1680700</td>\n",
       "      <td>20.6742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>21.330</td>\n",
       "      <td>21.375</td>\n",
       "      <td>21.110</td>\n",
       "      <td>21.185</td>\n",
       "      <td>5476800</td>\n",
       "      <td>20.7624</td>\n",
       "      <td>23717.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>21.000</td>\n",
       "      <td>21.410</td>\n",
       "      <td>20.995</td>\n",
       "      <td>21.325</td>\n",
       "      <td>6261600</td>\n",
       "      <td>20.8996</td>\n",
       "      <td>6795.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>21.440</td>\n",
       "      <td>21.450</td>\n",
       "      <td>20.705</td>\n",
       "      <td>20.880</td>\n",
       "      <td>5168400</td>\n",
       "      <td>20.4635</td>\n",
       "      <td>9348.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close   Volume  Adj Close   length  \\\n",
       "date                                                                      \n",
       "2016-01-01  21.095  21.095  21.095  21.095        0    20.6742      0.0   \n",
       "2015-12-31  21.135  21.140  20.915  21.095  1680700    20.6742      0.0   \n",
       "2015-12-30  21.330  21.375  21.110  21.185  5476800    20.7624  23717.0   \n",
       "2015-12-29  21.000  21.410  20.995  21.325  6261600    20.8996   6795.0   \n",
       "2015-12-28  21.440  21.450  20.705  20.880  5168400    20.4635   9348.0   \n",
       "\n",
       "            positivity  negativity  visibility  \n",
       "date                                            \n",
       "2016-01-01         0.0         0.0         0.0  \n",
       "2015-12-31         0.0         0.0         0.0  \n",
       "2015-12-30        18.0       -21.0         7.0  \n",
       "2015-12-29         6.0        -6.0         2.0  \n",
       "2015-12-28         9.0       -14.0         5.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mediastock[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to start the VAR analysis!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stationarity\n",
    "The first thing you have to do is checking for stationarity as VAR requires all time series to be stationary. To test for this, Dickey-Fuller (DF) tests are conducted for all series under investigation. If the tests do not reject the null hypothesis that assumes time series to be non-stationary, the series need to be differenced to achieve stationarity of the series. After differencing, the DF tests usually yield significant results, meaning we have reached stationary of the series.\n",
    "Let us first look into the variable we are most interested in, the stock prices. You could run\n",
    "help(sm.tsa.stattools.adfuller)\n",
    "to learn more about every detail, but for now, it might be enough to know that the first line gives you the Z-score, whole the second returned value is the p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.5515110941521715,\n",
       " 0.10343866512202399,\n",
       " 1,\n",
       " 521,\n",
       " {'1%': -3.4429638415440995,\n",
       "  '10%': -2.5697331315829222,\n",
       "  '5%': -2.8671034821276349},\n",
       " 404.34278073538189)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.tsa.stattools.adfuller(mediastock['Adj Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We see that p=.10, which is not significant. Therefore, we need to *difference* the series. \n",
    "\n",
    "But lets first check the other variables, and let's write a nice loop that prints out only the p-values in a clear way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positivity 2.84712704019e-30\n",
      "negativity 3.83303299318e-30\n",
      "visibility 5.00238766254e-30\n"
     ]
    }
   ],
   "source": [
    "mediavars=['positivity','negativity','visibility']\n",
    "for m in mediavars:\n",
    "    print(m,sm.tsa.stattools.adfuller(mediastock[m])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These series are clearly stationary, so usually we would NOT need to difference. But because *all* series have to be stationary, and one series (the stock price) is not, we have to differentiate anyway.\n",
    "To avoid any confusion (and because our dataset is so small that we don't care about the extra memory here), we create a new dataframe for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mediastockdiff=mediastock\n",
    "mediastockdiff['Adj Close']=mediastockdiff['Adj Close'].diff()\n",
    "mediastockdiff['visibility']=mediastockdiff['visibility'].diff()\n",
    "mediastockdiff['positivity']=mediastockdiff['positivity'].diff()\n",
    "mediastockdiff['negativity']=mediastockdiff['negativity'].diff()\n",
    "\n",
    "# Of course, it would have been more efficient to write a loop again for this, \n",
    "# but there needs some room for you to improve this template ;-)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of differentiation, the first row of the differentiated values now is NaN ('not a number', missing). Make sure to take that into account in future analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>length</th>\n",
       "      <th>positivity</th>\n",
       "      <th>negativity</th>\n",
       "      <th>visibility</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>21.095</td>\n",
       "      <td>21.095</td>\n",
       "      <td>21.095</td>\n",
       "      <td>21.095</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>21.135</td>\n",
       "      <td>21.140</td>\n",
       "      <td>20.915</td>\n",
       "      <td>21.095</td>\n",
       "      <td>1680700</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>21.330</td>\n",
       "      <td>21.375</td>\n",
       "      <td>21.110</td>\n",
       "      <td>21.185</td>\n",
       "      <td>5476800</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>23717.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>21.000</td>\n",
       "      <td>21.410</td>\n",
       "      <td>20.995</td>\n",
       "      <td>21.325</td>\n",
       "      <td>6261600</td>\n",
       "      <td>0.1372</td>\n",
       "      <td>6795.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>21.440</td>\n",
       "      <td>21.450</td>\n",
       "      <td>20.705</td>\n",
       "      <td>20.880</td>\n",
       "      <td>5168400</td>\n",
       "      <td>-0.4361</td>\n",
       "      <td>9348.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close   Volume  Adj Close   length  \\\n",
       "date                                                                      \n",
       "2016-01-01  21.095  21.095  21.095  21.095        0        NaN      0.0   \n",
       "2015-12-31  21.135  21.140  20.915  21.095  1680700     0.0000      0.0   \n",
       "2015-12-30  21.330  21.375  21.110  21.185  5476800     0.0882  23717.0   \n",
       "2015-12-29  21.000  21.410  20.995  21.325  6261600     0.1372   6795.0   \n",
       "2015-12-28  21.440  21.450  20.705  20.880  5168400    -0.4361   9348.0   \n",
       "\n",
       "            positivity  negativity  visibility  \n",
       "date                                            \n",
       "2016-01-01         NaN         NaN         NaN  \n",
       "2015-12-31         0.0         0.0         0.0  \n",
       "2015-12-30        18.0       -21.0         7.0  \n",
       "2015-12-29       -12.0        15.0        -5.0  \n",
       "2015-12-28         3.0        -8.0         3.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mediastockdiff[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In the particular domain of this dataset (predicting stock volatility), it seems to be common to not predict the direction of change, but only the absolute value of the change, irrespective of whether it is going up or down. So, that's what we're going to do -- but feel free to try out what happens if you use the original value instead!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mediastockdiff['Adj Close_abs']=mediastockdiff['Adj Close'].abs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be absolutely sure, let's check again for stationarity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adj Close 0.0\n",
      "Adj Close_abs 1.88233339063e-16\n",
      "positivity 7.32004249564e-16\n",
      "negativity 5.50684214047e-17\n",
      "visibility 2.72988524093e-17\n"
     ]
    }
   ],
   "source": [
    "# .ix[2:] slices the dataframe (in effect leaves away row number 0 and 1 which is NAN now b/c of differentiation)\n",
    "\n",
    "allvars=['Adj Close','Adj Close_abs','positivity','negativity','visibility']\n",
    "for m in allvars:\n",
    "    print(m, sm.tsa.stattools.adfuller(mediastockdiff.ix[2:][m])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Great! Everything looks good now, all series are stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lag selection\n",
    "\n",
    "In the next step, the number of lags of the variables in the model needs to be specified. This decision should be mainly triggered by theory. Furthermore, you can look at the fit-statistics such as the AIC and BIC to choose the number of lags that suggests the best model fit. The asterisks in the output of the selection-order criteria usually indicate the optimal number of lags for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 VAR Order Selection                  \n",
      "======================================================\n",
      "            aic          bic          fpe         hqic\n",
      "------------------------------------------------------\n",
      "0       -0.7696      -0.7529       0.4632      -0.7631\n",
      "1       -0.9951      -0.9450       0.3697      -0.9754\n",
      "2        -1.079      -0.9960       0.3398       -1.047\n",
      "3        -1.118      -1.001*       0.3269       -1.072\n",
      "4        -1.151       -1.001       0.3163      -1.092*\n",
      "5        -1.161      -0.9772       0.3133       -1.089\n",
      "6        -1.169      -0.9519       0.3108       -1.084\n",
      "7        -1.180      -0.9297       0.3073       -1.082\n",
      "8        -1.185      -0.9015       0.3057       -1.074\n",
      "9        -1.193      -0.8765       0.3032       -1.069\n",
      "10       -1.185      -0.8343       0.3059       -1.047\n",
      "11       -1.186      -0.8022       0.3055       -1.035\n",
      "12       -1.188      -0.7705       0.3050       -1.024\n",
      "13       -1.204      -0.7540       0.2999       -1.028\n",
      "14      -1.205*      -0.7208      0.2999*       -1.015\n",
      "15       -1.191      -0.6743       0.3039      -0.9886\n",
      "======================================================\n",
      "* Minimum\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'aic': 14, 'bic': 3, 'fpe': 14, 'hqic': 4}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsub = mediastockdiff.ix[1:][['visibility','Adj Close_abs']]\n",
    "\n",
    "model = sm.tsa.VAR(dfsub)\n",
    "model.select_order(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the AIC, we conclude that 14 lags are most appropriate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating a VAR model\n",
    "\n",
    "We now can estimate a VAR model with 14 lags:\n",
    "To do so, again, a subset of the data is created to use only the variables of interest for this model. Also, remember to leave away row number 0 ([1:]) as it contains NAN after differencing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Wed, 02, Nov, 2016\n",
       "Time:                     21:13:12\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         2.00000    BIC:                  -0.721899\n",
       "Nobs:                     508.000    HQIC:                  -1.01550\n",
       "Log likelihood:          -1077.60    FPE:                   0.299794\n",
       "AIC:                     -1.20491    Det(Omega_mle):        0.268289\n",
       "--------------------------------------------------------------------\n",
       "Results for equation visibility\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     0.325729         0.238591            1.365           0.173\n",
       "L1.visibility            -0.719514         0.045303          -15.882           0.000\n",
       "L1.Adj Close_abs         -0.128678         0.411282           -0.313           0.755\n",
       "L2.visibility            -0.663253         0.056255          -11.790           0.000\n",
       "L2.Adj Close_abs          0.394858         0.410851            0.961           0.337\n",
       "L3.visibility            -0.640975         0.064160           -9.990           0.000\n",
       "L3.Adj Close_abs         -0.022661         0.412723           -0.055           0.956\n",
       "L4.visibility            -0.633354         0.070262           -9.014           0.000\n",
       "L4.Adj Close_abs          0.011000         0.412957            0.027           0.979\n",
       "L5.visibility            -0.570956         0.075146           -7.598           0.000\n",
       "L5.Adj Close_abs         -0.125370         0.413440           -0.303           0.762\n",
       "L6.visibility            -0.546794         0.078185           -6.994           0.000\n",
       "L6.Adj Close_abs         -0.171643         0.413544           -0.415           0.678\n",
       "L7.visibility            -0.531385         0.079281           -6.703           0.000\n",
       "L7.Adj Close_abs         -0.381142         0.408183           -0.934           0.351\n",
       "L8.visibility            -0.441287         0.079577           -5.545           0.000\n",
       "L8.Adj Close_abs          0.128300         0.408209            0.314           0.753\n",
       "L9.visibility            -0.378328         0.078973           -4.791           0.000\n",
       "L9.Adj Close_abs          0.464334         0.406417            1.143           0.254\n",
       "L10.visibility           -0.267104         0.076408           -3.496           0.001\n",
       "L10.Adj Close_abs        -0.461457         0.405965           -1.137           0.256\n",
       "L11.visibility           -0.245512         0.071477           -3.435           0.001\n",
       "L11.Adj Close_abs        -0.177633         0.405880           -0.438           0.662\n",
       "L12.visibility           -0.162328         0.066172           -2.453           0.015\n",
       "L12.Adj Close_abs        -0.511393         0.405642           -1.261           0.208\n",
       "L13.visibility           -0.085025         0.058495           -1.454           0.147\n",
       "L13.Adj Close_abs        -0.228746         0.405633           -0.564           0.573\n",
       "L14.visibility           -0.130318         0.046909           -2.778           0.006\n",
       "L14.Adj Close_abs        -0.161070         0.403002           -0.400           0.690\n",
       "====================================================================================\n",
       "\n",
       "Results for equation Adj Close_abs\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     0.080844         0.026402            3.062           0.002\n",
       "L1.visibility             0.014582         0.005013            2.909           0.004\n",
       "L1.Adj Close_abs          0.173115         0.045512            3.804           0.000\n",
       "L2.visibility             0.021051         0.006225            3.382           0.001\n",
       "L2.Adj Close_abs          0.110664         0.045464            2.434           0.015\n",
       "L3.visibility             0.021067         0.007100            2.967           0.003\n",
       "L3.Adj Close_abs          0.084456         0.045672            1.849           0.065\n",
       "L4.visibility             0.016985         0.007775            2.184           0.029\n",
       "L4.Adj Close_abs          0.052327         0.045698            1.145           0.253\n",
       "L5.visibility             0.020475         0.008316            2.462           0.014\n",
       "L5.Adj Close_abs          0.013551         0.045751            0.296           0.767\n",
       "L6.visibility             0.013476         0.008652            1.558           0.120\n",
       "L6.Adj Close_abs          0.063519         0.045762            1.388           0.166\n",
       "L7.visibility             0.019636         0.008773            2.238           0.026\n",
       "L7.Adj Close_abs          0.029743         0.045169            0.658           0.511\n",
       "L8.visibility             0.021021         0.008806            2.387           0.017\n",
       "L8.Adj Close_abs         -0.024557         0.045172           -0.544           0.587\n",
       "L9.visibility             0.015635         0.008739            1.789           0.074\n",
       "L9.Adj Close_abs         -0.037003         0.044974           -0.823           0.411\n",
       "L10.visibility            0.017235         0.008455            2.038           0.042\n",
       "L10.Adj Close_abs        -0.029377         0.044924           -0.654           0.513\n",
       "L11.visibility            0.015305         0.007910            1.935           0.054\n",
       "L11.Adj Close_abs         0.049827         0.044914            1.109           0.268\n",
       "L12.visibility            0.015522         0.007323            2.120           0.035\n",
       "L12.Adj Close_abs         0.058498         0.044888            1.303           0.193\n",
       "L13.visibility            0.013834         0.006473            2.137           0.033\n",
       "L13.Adj Close_abs         0.135175         0.044887            3.011           0.003\n",
       "L14.visibility           -0.001334         0.005191           -0.257           0.797\n",
       "L14.Adj Close_abs         0.005708         0.044596            0.128           0.898\n",
       "====================================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "                 visibility  Adj Close_abs\n",
       "visibility         1.000000      -0.013710\n",
       "Adj Close_abs     -0.013710       1.000000\n",
       "\n"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsub = mediastockdiff.ix[1:][['visibility','Adj Close_abs']]\n",
    "model = sm.tsa.VAR(dfsub)\n",
    "results = model.fit(14)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next thing we want to do is to test the Granger Causaility to find out if visibility leads to market volatility or the other way around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger causality f-test\n",
      "==============================================================\n",
      "   Test statistic   Critical Value          p-value         df\n",
      "--------------------------------------------------------------\n",
      "         1.745440         1.702102            0.042  (14, 958)\n",
      "==============================================================\n",
      "H_0: ['visibility'] do not Granger-cause Adj Close_abs\n",
      "Conclusion: reject H_0 at 5.00% significance level\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'conclusion': 'reject',\n",
       " 'crit_value': 1.7021015432469633,\n",
       " 'df': (14, 958),\n",
       " 'pvalue': 0.042373784148657924,\n",
       " 'signif': 0.05,\n",
       " 'statistic': 1.7454396889704096}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.test_causality('Adj Close_abs',['visibility'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "It seems that visibility Granger-causes price volatility, as we reject H0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger causality f-test\n",
      "==============================================================\n",
      "   Test statistic   Critical Value          p-value         df\n",
      "--------------------------------------------------------------\n",
      "         0.680673         1.702102            0.795  (14, 958)\n",
      "==============================================================\n",
      "H_0: ['Adj Close_abs'] do not Granger-cause visibility\n",
      "Conclusion: fail to reject H_0 at 5.00% significance level\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'conclusion': 'fail to reject',\n",
       " 'crit_value': 1.7021015432469633,\n",
       " 'df': (14, 958),\n",
       " 'pvalue': 0.79487167664391412,\n",
       " 'signif': 0.05,\n",
       " 'statistic': 0.68067331226199224}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.test_causality('visibility','Adj Close_abs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not find an effect from volatility on visibility, because we cannot reject H0. This is pretty much what we expected, as we expected that stock exchange prices react to media coverage, rather than media coverage reacting to stock volatility.\n",
    "\n",
    "However, all of this is rather hard to see on the plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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koaNFT73c26KIoqcToeghhJDmQ0sPISQPFD2gpYeUg2t6CCGk+VD0EELyQNGD2htKWno6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\n8slk+6e8/e1yHlvGFc3Tu+6Stt8+H22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XN8qylh49dvPmZEfkk0f0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\n8uJ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+n0R0hYq3XX11//lV3LPp6exUR2inpyjeZv/djEjZdZk2eaKn10ujbVXibea8zFXT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\nd/g5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d98d8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next question to answer is how long the effects last. This can be done by plotting the impulse response functions for the appropriate number of lags. \n",
    "The *cumulative impulse response function* (CIRF) summarizes the additional increase of the dependent variable after there is a one-unit increase in the independent variable. For example, an additional one-unit increase of X results in a significant increase(decrease) of .075 points of variable Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/lib/twodim_base.py:233: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  m = zeros((N, M), dtype=dtype)\n",
      "/usr/local/lib/python3.5/dist-packages/numpy/lib/twodim_base.py:240: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  m[:M-k].flat[i::M+1] = 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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QoNTvYIuURSqyI/juu//5HUFEREQS3JYtWzjrrLOiagZet25dHn30Udq0aVMK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\nMzz5JHzwAZx1FlxxhXMPdwjPmxVwgwYN4qyzzgrIvp966imWLVtGo0aNA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vr27eunZNXP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\nR7b1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d9764518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irf = results.irf(14)\n",
    "irf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UMOv+5K/rWEBflJpm9qiZrTKz3WaWY2bvmtkfQsoU1D+loZm94jf32uTvJynf8Yvs2xKm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apVvPXWW7Rp00Y9D2ZBhw4dGD16NP/973/ZsWMHmzdv5q233uKOO+6gbdu29OjRI+gQ89zu3XDZ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QoICKYxERSTUVx2nAwoUL2b17N9OnT3c7Spr1ySfQvj3kyvXPYdSplS4d3Huv8+rUydl29Ojf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2db5a7e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irf.plot_cum_effects()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have concluded that media attention Granger causes stock price volatility, the bottom left graphs are most interesting for us.\n",
    "To identify the exact CIRF value have to investigate the values of CIRF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.        ,  0.        ],\n",
       "        [ 0.        ,  1.        ]],\n",
       "\n",
       "       [[ 0.28048557, -0.12867787],\n",
       "        [ 0.0145817 ,  1.17311504]],\n",
       "\n",
       "       [[ 0.13305705,  0.33648917],\n",
       "        [ 0.027665  ,  1.31187104]],\n",
       "\n",
       "       [[ 0.07945376,  0.11498079],\n",
       "        [ 0.03531454,  1.44357964]],\n",
       "\n",
       "       [[ 0.04749218,  0.09323279],\n",
       "        [ 0.03725976,  1.5525341 ]],\n",
       "\n",
       "       [[ 0.08792551, -0.04948877],\n",
       "        [ 0.04386472,  1.62293381]],\n",
       "\n",
       "       [[ 0.06984184, -0.17238164],\n",
       "        [ 0.04177942,  1.7294881 ]],\n",
       "\n",
       "       [[ 0.05546165, -0.4454798 ],\n",
       "        [ 0.04974236,  1.81321092]],\n",
       "\n",
       "       [[ 0.11366068, -0.06882544],\n",
       "        [ 0.05644465,  1.83159453]],\n",
       "\n",
       "       [[ 0.1301192 ,  0.38004698],\n",
       "        [ 0.05628911,  1.82716956]],\n",
       "\n",
       "       [[ 0.2002238 , -0.31925599],\n",
       "        [ 0.06023265,  1.8205212 ]],\n",
       "\n",
       "       [[ 0.14005431, -0.30992317],\n",
       "        [ 0.06260012,  1.86842338]],\n",
       "\n",
       "       [[ 0.16748297, -0.67452881],\n",
       "        [ 0.06661536,  1.93540898]],\n",
       "\n",
       "       [[ 0.18042874, -0.60776999],\n",
       "        [ 0.07084579,  2.09019034]],\n",
       "\n",
       "       [[ 0.07006137, -0.5184009 ],\n",
       "        [ 0.06278935,  2.15444178]]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irf.cum_effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.04530319,  0.41128177],\n",
       "        [ 0.00501322,  0.04551215]],\n",
       "\n",
       "       [[ 0.04714148,  0.4209526 ],\n",
       "        [ 0.00776845,  0.0699251 ]],\n",
       "\n",
       "       [[ 0.04739927,  0.42141438],\n",
       "        [ 0.01025895,  0.0917679 ]],\n",
       "\n",
       "       [[ 0.04724167,  0.41960847],\n",
       "        [ 0.01261818,  0.11254497]],\n",
       "\n",
       "       [[ 0.04696843,  0.41955795],\n",
       "        [ 0.01483129,  0.13236209]],\n",
       "\n",
       "       [[ 0.04633971,  0.42155786],\n",
       "        [ 0.01683626,  0.15130477]],\n",
       "\n",
       "       [[ 0.04544735,  0.41640578],\n",
       "        [ 0.0186532 ,  0.16981382]],\n",
       "\n",
       "       [[ 0.04461496,  0.41716871],\n",
       "        [ 0.02030445,  0.18820762]],\n",
       "\n",
       "       [[ 0.04447911,  0.41765417],\n",
       "        [ 0.0217509 ,  0.20517853]],\n",
       "\n",
       "       [[ 0.04419034,  0.41874189],\n",
       "        [ 0.022853  ,  0.22016744]],\n",
       "\n",
       "       [[ 0.04382003,  0.42415739],\n",
       "        [ 0.02356775,  0.23371871]],\n",
       "\n",
       "       [[ 0.04365064,  0.4238062 ],\n",
       "        [ 0.02411533,  0.24663573]],\n",
       "\n",
       "       [[ 0.04302461,  0.42365762],\n",
       "        [ 0.02433158,  0.25897127]],\n",
       "\n",
       "       [[ 0.04202668,  0.41234724],\n",
       "        [ 0.02419526,  0.27121147]]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irf.cum_effect_stderr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Similarly to the graph, we are interested in the influence of media attention on stock price volatility. We also want to know the *cummulative effect* - effect after a certain number of lags. Thereofre, we need to look at the last four items in the list, and more specifically the bottom left number. We can conclude that an additional one-unit increase of media attention (one additional article published) results in a significant increase of .063 points of stock volatility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conclude if the CIRF is stable we investigate the confidnece intervals. We can see them in the graph above and calculate them from the standard error of the CIRF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we compute the *forecast error variance* (FEV), which is a good indicator to show how much of the movement of\n",
    "one variable is attributed by shocks of its own past and to what extent by shocks of the other variables. The method can show how much variation of one variable can be explained by its own past or by another variable. \n",
    "Similarly to the CIRF, the appropriate number of lags is selected for this analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEVD for visibility\n",
      "      visibility  Adj Close_abs\n",
      "0       1.000000       0.000000\n",
      "1       0.999866       0.000134\n",
      "2       0.998151       0.001849\n",
      "3       0.997766       0.002234\n",
      "4       0.997764       0.002236\n",
      "5       0.997606       0.002394\n",
      "6       0.997487       0.002513\n",
      "7       0.996900       0.003100\n",
      "8       0.995793       0.004207\n",
      "9       0.994217       0.005783\n",
      "10      0.990442       0.009558\n",
      "11      0.990463       0.009537\n",
      "12      0.989445       0.010555\n",
      "13      0.989411       0.010589\n",
      "\n",
      "FEVD for Adj Close_abs\n",
      "      visibility  Adj Close_abs\n",
      "0       0.000188       0.999812\n",
      "1       0.016176       0.983824\n",
      "2       0.028221       0.971779\n",
      "3       0.031775       0.968225\n",
      "4       0.031662       0.968338\n",
      "5       0.034498       0.965502\n",
      "6       0.034504       0.965496\n",
      "7       0.038518       0.961482\n",
      "8       0.041549       0.958451\n",
      "9       0.041550       0.958450\n",
      "10      0.042612       0.957388\n",
      "11      0.042884       0.957116\n",
      "12      0.043750       0.956250\n",
      "13      0.043918       0.956082\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fevd = results.fevd(14)\n",
    "fevd.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Looking at the \"FEVD for Adj Close_abs\" value at lag 14 we can conclude that 4.39% of variation in stock price volatility can be explaied by media attenion within two weeks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conclude that the constructed VAR model is robust several tests need to be conducted.\n",
    "\n",
    "First, before interpreting the results of the VAR analysis, one has to test the residuals of the time series for non-autocorrelation. This needs to be done in order to be sure that the series adequately reflects the series¹ past. The null hypothesis of the Portmonteau (Q) test assumes the residuals to be white noise. Hence, we aim for accepting the null hypothesis (look for non-significant result). If this is not achieved, it can be considered to add additional lags to the model (however, keep in mind that the number of lags should be rather kept low in order to prevent spurious relations).\n",
    "\n",
    "The comand below returns residuals values needed in robustness checks. You can use them in later tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visibility</th>\n",
       "      <th>Adj Close_abs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>-1.458997</td>\n",
       "      <td>0.274699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>0.255809</td>\n",
       "      <td>0.587528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>1.734854</td>\n",
       "      <td>-0.309604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>-0.286063</td>\n",
       "      <td>-0.077404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>2.595280</td>\n",
       "      <td>-0.199739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>-2.121594</td>\n",
       "      <td>0.489160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>-0.532777</td>\n",
       "      <td>-0.111452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>-0.283896</td>\n",
       "      <td>0.552725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>-1.127898</td>\n",
       "      <td>-0.257389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>1.789499</td>\n",
       "      <td>-0.172119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>-3.532149</td>\n",
       "      <td>-0.120427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-26</th>\n",
       "      <td>-1.699452</td>\n",
       "      <td>-0.050793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-25</th>\n",
       "      <td>0.783562</td>\n",
       "      <td>0.033116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-24</th>\n",
       "      <td>0.617430</td>\n",
       "      <td>-0.140067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-23</th>\n",
       "      <td>-0.282545</td>\n",
       "      <td>-0.207914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-20</th>\n",
       "      <td>-1.172844</td>\n",
       "      <td>0.001074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-19</th>\n",
       "      <td>0.027023</td>\n",
       "      <td>0.223211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-18</th>\n",
       "      <td>-1.908537</td>\n",
       "      <td>0.039023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-17</th>\n",
       "      <td>-0.832390</td>\n",
       "      <td>-0.018100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-16</th>\n",
       "      <td>-0.446296</td>\n",
       "      <td>0.402918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-13</th>\n",
       "      <td>-2.175387</td>\n",
       "      <td>-0.244080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-12</th>\n",
       "      <td>-0.186921</td>\n",
       "      <td>-0.023625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-11</th>\n",
       "      <td>0.555292</td>\n",
       "      <td>0.776095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>-0.042271</td>\n",
       "      <td>0.034975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-09</th>\n",
       "      <td>-2.081731</td>\n",
       "      <td>-0.383750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-06</th>\n",
       "      <td>1.158300</td>\n",
       "      <td>-0.138506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-05</th>\n",
       "      <td>2.004576</td>\n",
       "      <td>-0.145917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>1.236422</td>\n",
       "      <td>0.487016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>2.577615</td>\n",
       "      <td>-0.220486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>-0.634874</td>\n",
       "      <td>0.461445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-11</th>\n",
       "      <td>-1.744310</td>\n",
       "      <td>0.195368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-10</th>\n",
       "      <td>-1.383163</td>\n",
       "      <td>0.207604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-07</th>\n",
       "      <td>-1.430267</td>\n",
       "      <td>-0.185634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-06</th>\n",
       "      <td>-1.139678</td>\n",
       "      <td>-0.109387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-05</th>\n",
       "      <td>6.531670</td>\n",
       "      <td>-0.073603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-04</th>\n",
       "      <td>-2.159298</td>\n",
       "      <td>-0.175133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-03</th>\n",
       "      <td>-1.765044</td>\n",
       "      <td>-0.101716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>0.085935</td>\n",
       "      <td>0.145753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-30</th>\n",
       "      <td>6.682872</td>\n",
       "      <td>0.326059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-29</th>\n",
       "      <td>0.012924</td>\n",
       "      <td>-0.030231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-28</th>\n",
       "      <td>-2.413978</td>\n",
       "      <td>-0.206092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-27</th>\n",
       "      <td>-1.274623</td>\n",
       "      <td>-0.159116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-24</th>\n",
       "      <td>-1.184810</td>\n",
       "      <td>0.056960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-23</th>\n",
       "      <td>-1.154779</td>\n",
       "      <td>0.115939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-22</th>\n",
       "      <td>-1.752879</td>\n",
       "      <td>-0.086264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-21</th>\n",
       "      <td>-0.744648</td>\n",
       "      <td>0.108470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-20</th>\n",
       "      <td>8.182050</td>\n",
       "      <td>-0.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-17</th>\n",
       "      <td>6.049883</td>\n",
       "      <td>-0.231083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>-1.570992</td>\n",
       "      <td>-0.222032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>-2.159986</td>\n",
       "      <td>-0.164435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>2.458425</td>\n",
       "      <td>-0.135448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-13</th>\n",
       "      <td>-1.868415</td>\n",
       "      <td>-0.136044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>-1.624310</td>\n",
       "      <td>0.038361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>1.215631</td>\n",
       "      <td>0.255396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>-1.798663</td>\n",
       "      <td>-0.253722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>-1.445793</td>\n",
       "      <td>-0.142605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>-2.659654</td>\n",
       "      <td>0.035242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-1.950901</td>\n",
       "      <td>0.042381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>-2.280994</td>\n",
       "      <td>-0.069849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-2.579103</td>\n",
       "      <td>-0.096819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>508 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            visibility  Adj Close_abs\n",
       "date                                 \n",
       "2015-12-11   -1.458997       0.274699\n",
       "2015-12-10    0.255809       0.587528\n",
       "2015-12-09    1.734854      -0.309604\n",
       "2015-12-08   -0.286063      -0.077404\n",
       "2015-12-07    2.595280      -0.199739\n",
       "2015-12-04   -2.121594       0.489160\n",
       "2015-12-03   -0.532777      -0.111452\n",
       "2015-12-02   -0.283896       0.552725\n",
       "2015-12-01   -1.127898      -0.257389\n",
       "2015-11-30    1.789499      -0.172119\n",
       "2015-11-27   -3.532149      -0.120427\n",
       "2015-11-26   -1.699452      -0.050793\n",
       "2015-11-25    0.783562       0.033116\n",
       "2015-11-24    0.617430      -0.140067\n",
       "2015-11-23   -0.282545      -0.207914\n",
       "2015-11-20   -1.172844       0.001074\n",
       "2015-11-19    0.027023       0.223211\n",
       "2015-11-18   -1.908537       0.039023\n",
       "2015-11-17   -0.832390      -0.018100\n",
       "2015-11-16   -0.446296       0.402918\n",
       "2015-11-13   -2.175387      -0.244080\n",
       "2015-11-12   -0.186921      -0.023625\n",
       "2015-11-11    0.555292       0.776095\n",
       "2015-11-10   -0.042271       0.034975\n",
       "2015-11-09   -2.081731      -0.383750\n",
       "2015-11-06    1.158300      -0.138506\n",
       "2015-11-05    2.004576      -0.145917\n",
       "2015-11-04    1.236422       0.487016\n",
       "2015-11-03    2.577615      -0.220486\n",
       "2015-11-02   -0.634874       0.461445\n",
       "...                ...            ...\n",
       "2014-02-11   -1.744310       0.195368\n",
       "2014-02-10   -1.383163       0.207604\n",
       "2014-02-07   -1.430267      -0.185634\n",
       "2014-02-06   -1.139678      -0.109387\n",
       "2014-02-05    6.531670      -0.073603\n",
       "2014-02-04   -2.159298      -0.175133\n",
       "2014-02-03   -1.765044      -0.101716\n",
       "2014-01-31    0.085935       0.145753\n",
       "2014-01-30    6.682872       0.326059\n",
       "2014-01-29    0.012924      -0.030231\n",
       "2014-01-28   -2.413978      -0.206092\n",
       "2014-01-27   -1.274623      -0.159116\n",
       "2014-01-24   -1.184810       0.056960\n",
       "2014-01-23   -1.154779       0.115939\n",
       "2014-01-22   -1.752879      -0.086264\n",
       "2014-01-21   -0.744648       0.108470\n",
       "2014-01-20    8.182050      -0.177659\n",
       "2014-01-17    6.049883      -0.231083\n",
       "2014-01-16   -1.570992      -0.222032\n",
       "2014-01-15   -2.159986      -0.164435\n",
       "2014-01-14    2.458425      -0.135448\n",
       "2014-01-13   -1.868415      -0.136044\n",
       "2014-01-10   -1.624310       0.038361\n",
       "2014-01-09    1.215631       0.255396\n",
       "2014-01-08   -1.798663      -0.253722\n",
       "2014-01-07   -1.445793      -0.142605\n",
       "2014-01-06   -2.659654       0.035242\n",
       "2014-01-03   -1.950901       0.042381\n",
       "2014-01-02   -2.280994      -0.069849\n",
       "2014-01-01   -2.579103      -0.096819\n",
       "\n",
       "[508 rows x 2 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.resid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run the Portmonteau (Q) test for both variables in the model. The usual number of 20 lags is selected for this test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lag 1\t Ljung Box test: 0.009909034698841706\tp = 0.9207062476836734\n",
      "lag 2\t Ljung Box test: 0.12721383702126202\tp = 0.9383737844094868\n",
      "lag 3\t Ljung Box test: 0.15256866803995\tp = 0.984856517405008\n",
      "lag 4\t Ljung Box test: 0.15256964710653118\tp = 0.9972341411176813\n",
      "lag 5\t Ljung Box test: 0.16305375218840387\tp = 0.9994611708931804\n",
      "lag 6\t Ljung Box test: 0.25452455297252913\tp = 0.9996876592497317\n",
      "lag 7\t Ljung Box test: 0.5301900329504086\tp = 0.999328257171756\n",
      "lag 8\t Ljung Box test: 1.429404456448766\tp = 0.9938190228496728\n",
      "lag 9\t Ljung Box test: 1.5536609204377139\tp = 0.9967287573967166\n",
      "lag 10\t Ljung Box test: 1.7705995279596038\tp = 0.9978151688997599\n",
      "lag 11\t Ljung Box test: 1.8013457398128203\tp = 0.9990813874881178\n",
      "lag 12\t Ljung Box test: 1.8667759905031194\tp = 0.9995844447950801\n",
      "lag 13\t Ljung Box test: 1.8667811582301488\tp = 0.9998470103942607\n",
      "lag 14\t Ljung Box test: 2.151943428656646\tp = 0.9998698258730293\n",
      "lag 15\t Ljung Box test: 3.3755981930112737\tp = 0.9991714911676655\n",
      "lag 16\t Ljung Box test: 3.7443916582964847\tp = 0.99927759539129\n",
      "lag 17\t Ljung Box test: 5.079139502741405\tp = 0.9975384867672757\n",
      "lag 18\t Ljung Box test: 5.088176229163579\tp = 0.9987159884399145\n",
      "lag 19\t Ljung Box test: 6.0862240511585375\tp = 0.9977157490321753\n",
      "lag 20\t Ljung Box test: 6.872420752611267\tp = 0.9970829527073143\n"
     ]
    }
   ],
   "source": [
    "r=sm.stats.diagnostic.acorr_ljungbox(results.resid['Adj Close_abs'],lags=20)\n",
    "i=0\n",
    "for e in zip(r[0],r[1]):\n",
    "    i+=1\n",
    "    print('lag {}\\t Ljung Box test: {}\\tp = {}'.format(i,e[0],e[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above, we took some effort to print the results nicely. That's nice, but not neccessary. Let's do the same test vor the visibility variable without pretty-printing: (the first list of numbers contain the test statistics, the second the p values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  8.06018509e-04,   1.74741063e-01,   1.75157762e-01,\n",
       "          2.49424859e-01,   3.75438828e-01,   3.77061001e-01,\n",
       "          8.11909051e-01,   9.00679512e-01,   1.35328095e+00,\n",
       "          1.54815906e+00,   2.04475197e+00,   2.23157454e+00,\n",
       "          2.72095709e+00,   4.07805398e+00,   5.47591694e+00,\n",
       "          6.42605681e+00,   1.32464594e+01,   1.34991814e+01,\n",
       "          1.45796095e+01,   1.53209909e+01]),\n",
       " array([ 0.97735073,  0.9163375 ,  0.98149639,  0.99284071,  0.99597918,\n",
       "         0.99902976,  0.99732051,  0.99880135,  0.99809591,  0.99877737,\n",
       "         0.99833236,  0.99895961,  0.99876785,  0.99497809,  0.98727247,\n",
       "         0.9828053 ,  0.7195371 ,  0.76110681,  0.748916  ,  0.75775525]))"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.diagnostic.acorr_ljungbox(results.resid['visibility'],lags=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Test for autocorrelation indicates that residuals morror white noise (at 20th lang, absolute closing price: 6.87, ns; media attenton: 15.32, ns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the Portmonteau (Q) test is exercised again, nut now by looking at the squared residuals of the series. The null hypothesis is the same as above. If we have to reject the null-hypothesis, this means we have to deal with heteroscedastic data. In other words, the variability of the residuals of all the stock market data is not equally distributed over time, but dependent on the time of measurement. To overcome this problem, one can either look for more sophisticated time series models (ARCH), or taking logarithmic values of the series. If taking logarithmic values is still not solving the problem, one should report this in the results section and report the results with caution.\n",
    "\n",
    "To perform the next test we first need to create a list with squared residuals for the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HETEROSCEDASTICITY\n",
    "sres_adj_close=[]\n",
    "for line in results.resid['Adj Close_abs']:\n",
    "    r_s=line*line\n",
    "    #print(r_s)\n",
    "    sres_adj_close.append(r_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.43442224,   8.22674035,  35.26969798,  35.27257456,\n",
       "         36.09028323,  36.64674341,  37.21772496,  37.22792873,\n",
       "         37.97518371,  38.44129463,  39.28996976,  39.64562894,\n",
       "         43.28488208,  44.77393331,  46.20838018,  46.85687674,\n",
       "         63.44175708,  63.75254817,  63.94313348,  70.34074264]),\n",
       " array([  2.31044080e-01,   1.63525705e-02,   1.06844298e-07,\n",
       "          4.08335510e-07,   9.11121339e-07,   2.06327990e-06,\n",
       "          4.26555947e-06,   1.04493828e-05,   1.76109568e-05,\n",
       "          3.17915425e-05,   4.72868083e-05,   8.23237114e-05,\n",
       "          4.02918279e-05,   4.43865474e-05,   4.92614665e-05,\n",
       "          7.16878356e-05,   2.81083292e-07,   4.99513371e-07,\n",
       "          9.05776337e-07,   1.60222512e-07]))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.diagnostic.acorr_ljungbox(sres_adj_close,lags=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sres_aantal=[]\n",
    "for line in results.resid['visibility']:\n",
    "    r_s=line*line\n",
    "    sres_aantal.append(r_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.43198235,   4.89807865,   5.61552127,   5.69303952,\n",
       "          6.7631123 ,   6.77653363,   7.02345153,   7.57127944,\n",
       "          8.87771534,  13.22007824,  13.22007825,  16.87046793,\n",
       "         17.32307556,  17.56335   ,  19.7387624 ,  19.76959372,\n",
       "         24.58911899,  25.43803526,  25.52146006,  25.68735034]),\n",
       " array([ 0.0639453 ,  0.08637653,  0.13189013,  0.22327502,  0.23886243,\n",
       "         0.34200866,  0.42644219,  0.47642721,  0.44863797,  0.21162601,\n",
       "         0.27918496,  0.15453519,  0.18495269,  0.22739715,  0.18218476,\n",
       "         0.23077781,  0.10429151,  0.11331838,  0.14408406,  0.17638778]))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.diagnostic.acorr_ljungbox(sres_aantal,lags=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We cannot reject the null hypothesis for the absolute closing price, which means that for this variable we can observe heteroscedasticity. However, as it is a common phenomenon for stock maret data thus no additional transformations are necessary.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final test - the VAR stability test - checks the eigenvalue stability condition after estimating the VAR model. The modulus of each eigenvalue has to be less than 1 so that the VAR model can be considered stable. If the VAR model is considered stable, we can interpret the CIRF and FEVD as outlined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smtsa.vector_ar.var_model.VARProcess.is_stable(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returned value 'True' allows us to coclude that the model is stable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TONE\n",
    "\n",
    "Now it's your turn! There are some other variables in the dataset to play with, for example positivity and negativity. You might also think of combining them into some general measure of tone or of absolute emotionality. Good luck!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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