CFR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook needs all the estimates obtained by simply running the Python module that contains the funtions that I use (the pickle file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################\n",
    "# Notebook for \"Dissecting market expectations \n",
    "# in the cross-section of book-to-market ratios\"\n",
    "#\n",
    "# Critical Finance Review  - February 2021\n",
    "# Thiago de Oliveira Souza\n",
    "################################################\n",
    "\n",
    "# For the multiprocessing part:\n",
    "#import itertools\n",
    "#from functools import partial\n",
    "#import concurrent.futures\n",
    "#import time\n",
    "\n",
    "#Monitor progress loop\n",
    "from tqdm.notebook import tqdm as tqdm\n",
    "\n",
    "#import pickle\n",
    "import seaborn as sns\n",
    "#import ipystata\n",
    "#import pandas as pd\n",
    "#import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "#import os\n",
    "#import psycopg2 \n",
    "from dateutil.relativedelta import *\n",
    "from pandas.tseries.offsets import *\n",
    "#from scipy import stats\n",
    "import scipy.io\n",
    "#import statsmodels.api as sm\n",
    "#import multiprocessing\n",
    "\n",
    "# Import module that contains all the PLS functions that I wrote:\n",
    "#################################################################\n",
    "\n",
    "# Append path:\n",
    "import sys\n",
    "sys.path.append('../Mymodules')\n",
    "\n",
    "# Import\n",
    "from pls import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open pickle saved from running the module (long estimation time)\n",
    "pickle_incomplete = False\n",
    "\n",
    "\n",
    "local = os.path.join(os.getcwd(), \n",
    "                     'local', \n",
    "                     'CFR72.mod')\n",
    "                     #'CFR72i.mod')\n",
    "\n",
    "model_dic = pickle.load(open(local, 'rb'))\n",
    "\n",
    "# Sometimes output from the module has only recursive part\n",
    "if pickle_incomplete:\n",
    "    for key in tqdm(model_dic):\n",
    "        non_recursive_estimates(model_dic[key], Std2=True)\n",
    "\n",
    "    # Save\n",
    "    local = os.path.join(os.getcwd(), \n",
    "                         'local', \n",
    "                         'CFR72.mod')\n",
    "    pickle.dump(model_dic, open(local, 'wb')) \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General parameters to create and save the TEX tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tex files (to be created):\n",
    "save1 = os.path.join(os.getcwd(), 'Output general', 'Table1.tex')\n",
    "save2 = os.path.join(os.getcwd(), 'Output general', 'Table2.tex')\n",
    "\n",
    "# Dictionary with regressors' labels\n",
    "####################################\n",
    "\n",
    "# Table 1 CODE: (std,logBM)\n",
    "indices1 = ['std','logBM']\n",
    "reg_dic = {(True, True): '$bm_{\\sigma,t-1}$',\n",
    "           (True, False): '$BM_{\\sigma,t-1}$',\n",
    "           (False, True): '$bm_{t-1}$'\n",
    "          }\n",
    "\n",
    "# Table 2 CODE: (lagBM, minH)\n",
    "indices2 = ['lagBM','minH'] \n",
    "reg_dic2 = {(1, 0.8): '$bm_{\\sigma,t-1}$',\n",
    "           (0, 0.8): '$bm_{\\sigma,t}$',\n",
    "           (1, 0.2): '$bm_{\\sigma,t-1}$',\n",
    "           (0, 0.2): '$bm_{\\sigma,t}$',\n",
    "          }\n",
    "\n",
    "# Print-friendly column headers:\n",
    "columns_to_print = {'ISR2Y': '$IS_y$ $R^2$',\n",
    "                    'ISR2pY': '$p$',\n",
    "                    'OOSR2Y': '$OOS_y$ $R^2$',\n",
    "                    'OOSR2pY': '$p$',\n",
    "                    'ISR2M': '$IS_m$ $R^2$',\n",
    "                    'ISR2pM': '$p$',\n",
    "                    'OOSR2M': '$OOS_m$ $R^2$',\n",
    "                    'OOSR2pM': '$p$',\n",
    "                   }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to fill and save latex table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_table(pd_list, model_dic, reg_dic, columns_to_print, indices, save=False):\n",
    "    table = pd_list.copy()\n",
    "    #Convert dataframe to array of tuples:\n",
    "    YearKeys = list(table.itertuples(index=False, name=None))\n",
    "    \n",
    "    # Replace 12 by 1 in horizon, then crete new tupple\n",
    "    table['horizon'] = 1\n",
    "    MonthKeys = list(table.itertuples(index=False, name=None))\n",
    "    \n",
    "    # Now the monthly models\n",
    "    table['keyY'] = YearKeys\n",
    "    table['keyM'] = MonthKeys\n",
    "    \n",
    "    table['Regressors'] = list(table[indices].itertuples(index=False, name=None))\n",
    "    table['Regressors'] = table['Regressors'].map(reg_dic)\n",
    "    \n",
    "    table.set_index(['target', 'Regressors','Nx'], inplace=True)\n",
    "    table = table[['keyY','keyM']]\n",
    "    \n",
    "    for h in ['Y','M']:\n",
    "        table['ISR2'+h] = table['key'+h].apply(lambda key: model_dic[key].ISR2).apply('{:.2f}'.format)\n",
    "        table['ISR2p'+h] = table['key'+h].apply(lambda key: model_dic[key].pNW).apply('{:.2f}'.format)\n",
    "        table['OOSR2'+h] = table['key'+h].apply(lambda key: model_dic[key].OOSR230)\n",
    "        table['OOSR2p'+h] = np.where(table['OOSR2'+h] >=0,\n",
    "                                  table['key'+h].apply(lambda key: model_dic[key].pENC30),\n",
    "                                  '-')\n",
    "        # Now fix the format (not before, because of the p-value condition)\n",
    "        table['OOSR2'+h] = table['OOSR2'+h].apply('{:.2f}'.format)\n",
    "    \n",
    "    table.rename(columns=columns_to_print, inplace=True)\n",
    "    table.index.names = [None,None,None]\n",
    "    table.drop(['keyY','keyM'], axis=1, inplace=True)\n",
    "\n",
    "    if save is not False:\n",
    "        # Set alignment of columns (the indices are left, rest is right)\n",
    "        calig = 'lll'\n",
    "        for i in range(table.shape[1]):\n",
    "            calig = calig + 'r'\n",
    "            \n",
    "        textring = table.to_latex(escape=False,\n",
    "                                  column_format = calig,\n",
    "                                  multicolumn_format='c',\n",
    "                                  multirow=True,\n",
    "                                  #float_format = lambda x: '{:0.3f}'.format(x) if pd.notna(x) else '-',\n",
    "                                  na_rep = '-')\n",
    "        # Change to look better\n",
    "        textring = textring.replace('\\cline', '\\cmidrule')\n",
    "        textring = textring.replace('None', '')\n",
    "        \n",
    "        # Write string to file\n",
    "        print(save)\n",
    "        with open(save,'w') as tf:\n",
    "            tf.write(textring)\n",
    "    return table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tables 1 and 2\n",
    "\n",
    "1. Read the keys for all models in each table from the Excel file\n",
    "2. Fill the fields in the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tsouza\\OneDrive - Syddansk Universitet\\Python\\Disaggregate\\Output general\\Table1.tex\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$IS_y$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "      <th>$OOS_y$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "      <th>$IS_m$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "      <th>$OOS_m$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">RM</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{\\sigma,t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>8.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.77</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.67</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.06</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.73</td>\n",
       "      <td>&lt;0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>19.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.07</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.63</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>3.57</td>\n",
       "      <td>0.14</td>\n",
       "      <td>5.06</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.63</td>\n",
       "      <td>0.09</td>\n",
       "      <td>5.90</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>14.31</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.69</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">$BM_{\\sigma,t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>6.77</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.70</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>26.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">MP</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{\\sigma,t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.73</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.31</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.83</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>18.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.26</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>6.95</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-10.60</td>\n",
       "      <td>-</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-20.05</td>\n",
       "      <td>-</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>19.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>-</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">$BM_{\\sigma,t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>5.52</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.29</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-</td>\n",
       "      <td>2.86</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>27.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.25</td>\n",
       "      <td>-</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         $IS_y$ $R^2$   $p$ $OOS_y$ $R^2$    $p$ $IS_m$ $R^2$  \\\n",
       "RM $bm_{\\sigma,t-1}$ 6           8.53  0.00          6.77  <0.01         0.58   \n",
       "                     25         12.31  0.00          8.06  <0.01         0.89   \n",
       "                     100        19.83  0.00         14.07  <0.01         2.53   \n",
       "   $bm_{t-1}$        6           3.57  0.14          5.06  <0.05         0.21   \n",
       "                     25          5.63  0.09          5.90  <0.05         0.37   \n",
       "                     100        14.31  0.01          5.69  <0.05         2.25   \n",
       "   $BM_{\\sigma,t-1}$ 6           6.77  0.01          5.70  <0.01         0.27   \n",
       "                     25         19.51  0.00          0.40  <0.01         2.60   \n",
       "                     100        26.42  0.00         -2.86      -         3.24   \n",
       "MP $bm_{\\sigma,t-1}$ 6           5.92  0.02          0.73  <0.05         0.46   \n",
       "                     25          9.83  0.01          2.16  <0.01         0.74   \n",
       "                     100        18.14  0.00          9.26  <0.01         2.41   \n",
       "   $bm_{t-1}$        6           6.95  0.04        -10.60      -         0.76   \n",
       "                     25         14.40  0.00        -20.05      -         0.91   \n",
       "                     100        19.18  0.00         -7.60      -         2.34   \n",
       "   $BM_{\\sigma,t-1}$ 6           5.52  0.03          1.29  <0.05         0.81   \n",
       "                     25         19.71  0.00         -5.07      -         2.86   \n",
       "                     100        27.93  0.00         -7.25      -         3.02   \n",
       "\n",
       "                           $p$ $OOS_m$ $R^2$    $p$  \n",
       "RM $bm_{\\sigma,t-1}$ 6    0.03          0.67  <0.05  \n",
       "                     25   0.01          0.73  <0.01  \n",
       "                     100  0.01          0.63  <0.05  \n",
       "   $bm_{t-1}$        6    0.39         -0.05      -  \n",
       "                     25   0.20         -0.96      -  \n",
       "                     100  0.05         -0.61      -  \n",
       "   $BM_{\\sigma,t-1}$ 6    0.19          0.19      -  \n",
       "                     25   0.06         -0.07      -  \n",
       "                     100  0.04         -0.29      -  \n",
       "MP $bm_{\\sigma,t-1}$ 6    0.06          0.31  <0.05  \n",
       "                     25   0.02          0.09  <0.05  \n",
       "                     100  0.03         -0.19      -  \n",
       "   $bm_{t-1}$        6    0.01         -0.96      -  \n",
       "                     25   0.00         -2.55      -  \n",
       "                     100  0.04         -1.63      -  \n",
       "   $BM_{\\sigma,t-1}$ 6    0.05         -1.00      -  \n",
       "                     25   0.09         -0.42      -  \n",
       "                     100  0.06         -0.55      -  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read tupples\n",
    "T1 = pd.read_excel(os.path.normpath(os.path.join(os.getcwd(),\n",
    "                                                 \"Table model v3.xlsx\")),\n",
    "                   sheet_name='keysT1')\n",
    "#                   sheet_name='noLOG')\n",
    "\n",
    "fill_table(T1, model_dic, reg_dic, columns_to_print, indices1, save1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tsouza\\OneDrive - Syddansk Universitet\\Python\\Disaggregate\\Output general\\Table2.tex\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$IS_y$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "      <th>$OOS_y$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "      <th>$IS_m$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "      <th>$OOS_m$ $R^2$</th>\n",
       "      <th>$p$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">RM</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{\\sigma,t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>7.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.05</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.34</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.05</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>17.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.21</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.34</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{\\sigma,t}$</th>\n",
       "      <th>6</th>\n",
       "      <td>7.17</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.11</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>&lt;0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.24</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>18.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.66</td>\n",
       "      <td>&lt;0.01</td>\n",
       "      <td>3.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">MP</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{\\sigma,t-1}$</th>\n",
       "      <th>6</th>\n",
       "      <td>5.28</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-5.37</td>\n",
       "      <td>-</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>16.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.76</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">$bm_{\\sigma,t}$</th>\n",
       "      <th>6</th>\n",
       "      <td>5.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6.94</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>16.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.09</td>\n",
       "      <td>&lt;0.05</td>\n",
       "      <td>3.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         $IS_y$ $R^2$   $p$ $OOS_y$ $R^2$    $p$ $IS_m$ $R^2$  \\\n",
       "RM $bm_{\\sigma,t-1}$ 6           7.65  0.00          4.05  <0.01         0.40   \n",
       "                     25          9.59  0.00          2.05  <0.01         0.57   \n",
       "                     100        17.45  0.00          8.21  <0.01         2.61   \n",
       "   $bm_{\\sigma,t}$   6           7.17  0.01          4.11  <0.01         0.43   \n",
       "                     25          9.08  0.00          2.24  <0.01         1.11   \n",
       "                     100        18.39  0.00          8.66  <0.01         3.90   \n",
       "MP $bm_{\\sigma,t-1}$ 6           5.28  0.03         -2.66      -         0.28   \n",
       "                     25          7.21  0.02         -5.37      -         0.43   \n",
       "                     100        16.33  0.00          2.76  <0.05         2.53   \n",
       "   $bm_{\\sigma,t}$   6           5.02  0.03         -2.34      -         0.36   \n",
       "                     25          6.94  0.02         -5.03      -         1.39   \n",
       "                     100        16.44  0.00          3.09  <0.05         3.71   \n",
       "\n",
       "                           $p$ $OOS_m$ $R^2$    $p$  \n",
       "RM $bm_{\\sigma,t-1}$ 6    0.06          0.34  <0.05  \n",
       "                     25   0.02          0.15  <0.05  \n",
       "                     100  0.01          0.34  <0.05  \n",
       "   $bm_{\\sigma,t}$   6    0.05          0.14  <0.05  \n",
       "                     25   0.00         -0.50      -  \n",
       "                     100  0.00         -0.31      -  \n",
       "MP $bm_{\\sigma,t-1}$ 6    0.14         -0.10      -  \n",
       "                     25   0.06         -0.50      -  \n",
       "                     100  0.03         -0.35      -  \n",
       "   $bm_{\\sigma,t}$   6    0.11         -0.42      -  \n",
       "                     25   0.00         -1.72      -  \n",
       "                     100  0.00         -0.92      -  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2 = pd.read_excel(os.path.normpath(os.path.join(os.getcwd(),\n",
    "                                                 \"Table model v3.xlsx\")),\n",
    "                   sheet_name='keysT2')\n",
    "#                   sheet_name='no02')\n",
    "\n",
    "fill_table(T2, model_dic, reg_dic2, columns_to_print, indices2, save2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph cumulative sum of squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tsouza\\OneDrive - Syddansk Universitet\\Python\\Disaggregate\\Output general\\Main_Figure.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def graph_different_models(all_models_dic, keys_plot, LineTitle, ColTitle, SeriesLabels, SeriesLegs, Colors=False,\n",
    "                           InsideLegs=True, OutsideLegs=True, Ylims=False, s=False, lowD=0, hline=False):\n",
    "\n",
    "    if Ylims:\n",
    "        if len(Ylims) == 1:\n",
    "            Ylims = [Ylims[0] for i in range(len(keys_plot))]\n",
    "\n",
    "    ######################################\n",
    "    # Here the code starts\n",
    "    ######################################\n",
    "    plt.style.use('ggplot')\n",
    "    f, ax = plt.subplots(len(keys_plot), len(SeriesLabels),\n",
    "                         sharex=True, sharey=False, figsize=(12, len(SeriesLabels) * len(keys_plot)))\n",
    "    plt.subplots_adjust(wspace=0.1, hspace=0.15)\n",
    "    plt.set_cmap('Pastel1')\n",
    "    \n",
    "\n",
    "    for i in range(len(keys_plot)):\n",
    "        for j in range(len(SeriesLabels)):\n",
    "            ax[i,j].xaxis.set_major_locator(plt.MaxNLocator(9))\n",
    "            #if col == (len(SeriesLabels[0]) - 1):\n",
    "            #    ax[i,j].yaxis.set_label_position(\"right\")\n",
    "            #    ax[i,j].yaxis.tick_right()\n",
    "            \n",
    "            # graph is defined (i,j).\n",
    "            if hline is not False:\n",
    "                if hline[0][j] is not False:\n",
    "                    ax[i,j].axhline(y=hline[0][j], color='gray', linestyle='-')\n",
    "            if i == 0 and ColTitle:\n",
    "                ax[i,j].set(title=ColTitle[j])\n",
    "            if j == 0 and LineTitle:\n",
    "                ax[i,j].set_ylabel(LineTitle[i], fontsize='large')\n",
    "                #ax[i,j].set(ylabel=None)  # remove the y-axis label\n",
    "                ax[i,j].set_yticklabels([])\n",
    "                ax[i,j].tick_params(left=False)  # remove the ticks\n",
    "\n",
    "            \n",
    "            if Ylims[i][j]:\n",
    "                ax[i,j].axis(ymin=Ylims[i][j][0], ymax=Ylims[i][j][1])\n",
    "\n",
    "            # Inside the graph, may plot more than one model/key):\n",
    "            for key in keys_plot[i][j]:\n",
    "                # For each model may plot more than one series (depending on column):\n",
    "                SeriesLabels[j]\n",
    "                #for ks in range(len(SeriesLabels[i][j])):\n",
    "                ax[i,j].plot(all_models_dic[key].OOSstat[lowD:DateLim[key[-1]]].index,\n",
    "                             all_models_dic[key].OOSstat[lowD:DateLim[key[-1]]][SeriesLabels[j]],\n",
    "                             linewidth=1.5,\n",
    "                             color= Color[key[-1]],\n",
    "                             label=SeriesLegs[j][0][key[-1]] if i == 0 else None)\n",
    "                if SeriesLegs and InsideLegs:\n",
    "                    ax[i, j].legend()\n",
    "        if SeriesLegs is not False and OutsideLegs:\n",
    "            f.legend(loc='lower center', ncol=5)\n",
    "    f.align_labels()\n",
    "\n",
    "    if s:\n",
    "        print(s)\n",
    "        f.savefig(s, bbox_inches='tight')\n",
    "\n",
    "        \n",
    "        \n",
    "keys_plot = [[[(100,1,'RM',True,True,1,0.8,60,False)],\n",
    "              [(100,1,'RM',True,True,1,0.8,60,True), (100,1,'RM',True,True,1,0.8,60,False)]],\n",
    "             [[(100,12,'RM',True,True,1,0.8,60,False)],\n",
    "              [(100,12,'RM',True,True,1,0.8,60,True), (100,12,'RM',True,True,1,0.8,60,False)]],\n",
    "             [[(100,1,'MP',True,True,1,0.8,60,False)],\n",
    "              [(100,1,'MP',True,True,1,0.8,60,True), (100,1,'MP',True,True,1,0.8,60,False)]],\n",
    "             [[(100,12,'MP',True,True,1,0.8,60,False)],\n",
    "              [(100,12,'MP',True,True,1,0.8,60,True), (100,12,'MP',True,True,1,0.8,60,False)]]             \n",
    "            ]\n",
    "\n",
    "#keys_plot[0][1]\n",
    "\n",
    "SeriesLabels = ['cumSSE',\n",
    "                 'R2']\n",
    "\n",
    "#Colors = [[['navy'],\n",
    "#           ['orange','navy']]]\n",
    "\n",
    "SeriesLegs = [[{False: None,True: None}],\n",
    "              [{False: '1926-2019',True: '1930-2010'}]]\n",
    "#SeriesLegs[1][0][key[-1]]\n",
    "\n",
    "LineTitle =[r'$RM_m$',\n",
    "            r'$RM_y$',\n",
    "            r'$MP_m$',\n",
    "            r'$MP_y$',\n",
    "           ]\n",
    "\n",
    "DateLim = {False: '2020-01-31',\n",
    "           True: '2008-01-31'}\n",
    "\n",
    "Color = {False: 'navy',\n",
    "         True: 'orange'}\n",
    "#Color[key[-1]]\n",
    "\n",
    "Ylims = [[False, (-3,1)],\n",
    "         [False, (-40,20)],\n",
    "         [False, (-3,1)],\n",
    "         [False, (-40,20)]]\n",
    "\n",
    "hline = [[False,0]]\n",
    "\n",
    "\n",
    "ColTitle = ['cum. $\\Delta SSE$', 'OOS $R^2$']\n",
    "\n",
    "lowD='1945'\n",
    "s = os.path.join(os.getcwd(), 'Output general', 'Main_Figure.pdf')\n",
    "\n",
    "graph_different_models(all_models_dic=model_dic, keys_plot=keys_plot, LineTitle=LineTitle, ColTitle=ColTitle, \n",
    "                       SeriesLabels = SeriesLabels, SeriesLegs = SeriesLegs, Colors=False, InsideLegs=False,\n",
    "                       OutsideLegs=True, Ylims=Ylims, s=s, lowD=lowD, hline=hline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}