rework analizy panelowej z pracy licencjackiej.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from linearmodels import PanelOLS, PooledOLS\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style = 'darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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",
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       "\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>year</th>\n",
       "      <th>Primary Exchange</th>\n",
       "      <th>Offer Size (M)</th>\n",
       "      <th>Averege Offer Price</th>\n",
       "      <th>Offer To Date Average</th>\n",
       "      <th>Last Close Average</th>\n",
       "      <th>Last Price Average</th>\n",
       "      <th>Sum Offer Size (M)</th>\n",
       "      <th>Sum Offer Price</th>\n",
       "      <th>Sum Offer To Date</th>\n",
       "      <th>Sum Last Price</th>\n",
       "      <th>Sum of IPOs</th>\n",
       "      <th>Sum Same Market IPO</th>\n",
       "      <th>Foregin Market IPO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>BD</td>\n",
       "      <td>2.579239</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.431479</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>BD</td>\n",
       "      <td>32.428756</td>\n",
       "      <td>2.283333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.863000</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>BD</td>\n",
       "      <td>1176.447450</td>\n",
       "      <td>142.888889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.284760</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>BD</td>\n",
       "      <td>3085.569880</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1785</td>\n",
       "      <td>0.179</td>\n",
       "      <td>1.339090</td>\n",
       "      <td>3.20</td>\n",
       "      <td>-89.381060</td>\n",
       "      <td>0.0992</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>BD</td>\n",
       "      <td>1962.017912</td>\n",
       "      <td>20.504286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.6000</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2018</td>\n",
       "      <td>FN Iceland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.601420</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3206.428571</td>\n",
       "      <td>92.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2019</td>\n",
       "      <td>FN Iceland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.886250</td>\n",
       "      <td>6.40</td>\n",
       "      <td>799.562914</td>\n",
       "      <td>49.9600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2020</td>\n",
       "      <td>FN Iceland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.968430</td>\n",
       "      <td>3.50</td>\n",
       "      <td>-98.166971</td>\n",
       "      <td>0.0510</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2021</td>\n",
       "      <td>FN Iceland</td>\n",
       "      <td>15.396820</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>-3.466667</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>15.000</td>\n",
       "      <td>4.011240</td>\n",
       "      <td>3.10</td>\n",
       "      <td>19.354839</td>\n",
       "      <td>4.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2022</td>\n",
       "      <td>FN Iceland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.103450</td>\n",
       "      <td>5.00</td>\n",
       "      <td>-88.860486</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year Primary Exchange  Offer Size (M)  Averege Offer Price  \\\n",
       "0    2011               BD        2.579239             2.210000   \n",
       "1    2012               BD       32.428756             2.283333   \n",
       "2    2013               BD     1176.447450           142.888889   \n",
       "3    2014               BD     3085.569880            51.000000   \n",
       "4    2015               BD     1962.017912            20.504286   \n",
       "..    ...              ...             ...                  ...   \n",
       "196  2018       FN Iceland        0.000000                  NaN   \n",
       "197  2019       FN Iceland        0.000000                  NaN   \n",
       "198  2020       FN Iceland        0.000000                  NaN   \n",
       "199  2021       FN Iceland       15.396820            15.250000   \n",
       "200  2022       FN Iceland        0.000000                  NaN   \n",
       "\n",
       "     Offer To Date Average  Last Close Average  Last Price Average  \\\n",
       "0                      NaN                 NaN                 NaN   \n",
       "1                      NaN                 NaN                 NaN   \n",
       "2                      NaN                 NaN                 NaN   \n",
       "3                      NaN              0.1785               0.179   \n",
       "4                      NaN                 NaN                 NaN   \n",
       "..                     ...                 ...                 ...   \n",
       "196                    NaN                 NaN                 NaN   \n",
       "197                    NaN                 NaN                 NaN   \n",
       "198                    NaN                 NaN                 NaN   \n",
       "199              -3.466667             15.0000              15.000   \n",
       "200                    NaN                 NaN                 NaN   \n",
       "\n",
       "     Sum Offer Size (M)  Sum Offer Price  Sum Offer To Date  Sum Last Price  \\\n",
       "0              0.431479             2.13           0.000000          0.0000   \n",
       "1              0.863000             1.50           0.000000          0.0000   \n",
       "2              1.284760             3.00           0.000000          0.0000   \n",
       "3              1.339090             3.20         -89.381060          0.0992   \n",
       "4              0.000000             0.00           0.000000         34.6000   \n",
       "..                  ...              ...                ...             ...   \n",
       "196            3.601420            14.00        3206.428571         92.9000   \n",
       "197            3.886250             6.40         799.562914         49.9600   \n",
       "198            3.968430             3.50         -98.166971          0.0510   \n",
       "199            4.011240             3.10          19.354839          4.0500   \n",
       "200            4.103450             5.00         -88.860486          0.5100   \n",
       "\n",
       "     Sum of IPOs  Sum Same Market IPO  Foregin Market IPO  \n",
       "0              3                    0                   3  \n",
       "1              3                    0                   3  \n",
       "2              9                    0                   9  \n",
       "3             14                    0                  14  \n",
       "4             14                    0                  14  \n",
       "..           ...                  ...                 ...  \n",
       "196            0                    0                   0  \n",
       "197            0                    0                   0  \n",
       "198            0                    0                   0  \n",
       "199            2                    2                   0  \n",
       "200            0                    0                   0  \n",
       "\n",
       "[201 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bloomberg = pd.read_csv(\"C:/Users/Kacper/OneDrive/Dokumenty/Licencjat/NordicsandBalticsIPOs6.csv\",sep = \";\")\n",
    "bloomberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "Primary Exchange\n",
      "Offer Size (M)\n",
      "Averege Offer Price\n",
      "Offer To Date Average\n",
      "Last Close Average\n",
      "Last Price Average\n",
      "Sum Offer Size (M)\n",
      "Sum Offer Price\n",
      "Sum Offer To Date\n",
      "Sum Last Price\n",
      "Sum of IPOs\n",
      "Sum Same Market IPO\n",
      "Foregin Market IPO\n"
     ]
    }
   ],
   "source": [
    "for col in bloomberg:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1728x1152 with 0 Axes>"
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    {
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",
      "text/plain": [
       "<Figure size 468.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf()\n",
    "fig.set_size_inches(24, 16)\n",
    "sns.lmplot(x='Averege Offer Price', y='Foregin Market IPO', \n",
    "        hue='Primary Exchange', data = bloomberg);\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Series can only be used with a 2-level MultiIndex",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Kacper\\Documents\\Analiza statystyczna ekonometryczna w pythonie\\rework analizy panelowej z pracy licencjackiej.ipynb Komórka 6\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Kacper/Documents/Analiza%20statystyczna%20ekonometryczna%20w%20pythonie/rework%20analizy%20panelowej%20z%20pracy%20licencjackiej.ipynb#W5sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m exog \u001b[39m=\u001b[39m sm\u001b[39m.\u001b[39mtools\u001b[39m.\u001b[39mtools\u001b[39m.\u001b[39madd_constant(bloomberg[\u001b[39m'\u001b[39m\u001b[39mForegin Market IPO\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Kacper/Documents/Analiza%20statystyczna%20ekonometryczna%20w%20pythonie/rework%20analizy%20panelowej%20z%20pracy%20licencjackiej.ipynb#W5sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m endog \u001b[39m=\u001b[39m bloomberg[\u001b[39m'\u001b[39m\u001b[39mAverege Offer Price\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Kacper/Documents/Analiza%20statystyczna%20ekonometryczna%20w%20pythonie/rework%20analizy%20panelowej%20z%20pracy%20licencjackiej.ipynb#W5sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m mod \u001b[39m=\u001b[39m PooledOLS(endog, exog)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Kacper/Documents/Analiza%20statystyczna%20ekonometryczna%20w%20pythonie/rework%20analizy%20panelowej%20z%20pracy%20licencjackiej.ipynb#W5sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pooledOLS_res \u001b[39m=\u001b[39m mod\u001b[39m.\u001b[39mfit(cov_type\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mclustered\u001b[39m\u001b[39m'\u001b[39m, cluster_entity\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\Kacper\\anaconda3\\lib\\site-packages\\linearmodels\\panel\\model.py:855\u001b[0m, in \u001b[0;36mPooledOLS.__init__\u001b[1;34m(self, dependent, exog, weights, check_rank)\u001b[0m\n\u001b[0;32m    847\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m    848\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m    849\u001b[0m     dependent: PanelDataLike,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    853\u001b[0m     check_rank: \u001b[39mbool\u001b[39m \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m    854\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 855\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(dependent, exog, weights\u001b[39m=\u001b[39;49mweights, check_rank\u001b[39m=\u001b[39;49mcheck_rank)\n",
      "File \u001b[1;32mc:\\Users\\Kacper\\anaconda3\\lib\\site-packages\\linearmodels\\panel\\model.py:290\u001b[0m, in \u001b[0;36m_PanelModelBase.__init__\u001b[1;34m(self, dependent, exog, weights, check_rank)\u001b[0m\n\u001b[0;32m    282\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m    283\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m    284\u001b[0m     dependent: PanelDataLike,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    288\u001b[0m     check_rank: \u001b[39mbool\u001b[39m \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m    289\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 290\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdependent \u001b[39m=\u001b[39m PanelData(dependent, \u001b[39m\"\u001b[39;49m\u001b[39mDep\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m    291\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexog \u001b[39m=\u001b[39m PanelData(exog, \u001b[39m\"\u001b[39m\u001b[39mExog\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    292\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_original_shape \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdependent\u001b[39m.\u001b[39mshape\n",
      "File \u001b[1;32mc:\\Users\\Kacper\\anaconda3\\lib\\site-packages\\linearmodels\\panel\\data.py:214\u001b[0m, in \u001b[0;36mPanelData.__init__\u001b[1;34m(self, x, var_name, convert_dummies, drop_first, copy)\u001b[0m\n\u001b[0;32m    212\u001b[0m     x \u001b[39m=\u001b[39m DataFrame(x)\n\u001b[0;32m    213\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, Series):\n\u001b[1;32m--> 214\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mSeries can only be used with a 2-level MultiIndex\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    216\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x, DataFrame):\n\u001b[0;32m    217\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(x\u001b[39m.\u001b[39mindex, MultiIndex):\n",
      "\u001b[1;31mValueError\u001b[0m: Series can only be used with a 2-level MultiIndex"
     ]
    }
   ],
   "source": [
    "exog = sm.tools.tools.add_constant(bloomberg['Foregin Market IPO'])\n",
    "endog = bloomberg['Averege Offer Price']\n",
    "mod = PooledOLS(endog, exog)\n",
    "pooledOLS_res = mod.fit(cov_type='clustered', cluster_entity=True)"
   ]
  },
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