Diferencias_en_Diferencias.ipynb

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      "name": "Diferencias en Diferencias.ipynb",
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      "authorship_tag": "ABX9TyOX72pERywgaNs2lejKhN2A",
      "include_colab_link": true
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rpasquini/herramientas-econometricas/blob/main/Diferencias_en_Diferencias.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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        "id": "TqV_-YRrQ__N",
        "outputId": "3c59f4e8-6c15-4800-b85a-a0ea9efba596",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
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      },
      "source": [
        "!pip install geopandas"
      ],
      "execution_count": null,
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          "text": [
            "Collecting geopandas\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/a4/e66aafbefcbb717813bf3a355c8c4fc3ed04ea1dd7feb2920f2f4f868921/geopandas-0.8.1-py2.py3-none-any.whl (962kB)\n",
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            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.15.0)\n",
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          ],
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmYfGqZpdQBb"
      },
      "source": [
        "# Investigando efectos del COVID en alquileres usando Diferencias en Diferencias\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pO2zmpgUQ4WH"
      },
      "source": [
        "import geopandas as gpd\n",
        "import numpy as np\n",
        "data=gpd.GeoDataFrame.from_file(\"https://github.com/rpasquini/urban-econometrics/blob/master/data/deptos_amba_0519_0520.zip?raw=true\")\n",
        "df=data.loc[data.l2=='Capital Federal'] # vamos a trabajar con la Capital Federal"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP4vO2XJWmNV",
        "outputId": "ca7e6f27-33b2-4e0f-b1b9-b763e18d1f89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "df['airelibre']=np.where((df.patio=='1')| (df.terraza=='1'),1,0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/geopandas/geodataframe.py:853: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  super(GeoDataFrame, self).__setitem__(key, value)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAutci77gvsl"
      },
      "source": [
        "¿Cuánto fue el alquiler promedio de un departamento tomando el año completo?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbygBdRhgqnQ",
        "outputId": "6bd90f40-1a8f-45f9-9233-bcb178e1aab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.price.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25351.83177693887"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpsnXP2_dcly"
      },
      "source": [
        "Exploramos la diferencia en diferencia \\\n",
        "\n",
        "$E[y|t=aire,Post]-E[y|t=aire,Pre]=\\lambda_{POST}+\\beta-\\lambda_{PRE}$\n",
        "$E[y|t=sinaire,Post]-E[y|t=sinaire,Pre]=\\lambda_{POST}-\\lambda_{PRE}$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vh7zXzmxdAMT",
        "outputId": "ef7855ea-a3e2-4154-e15a-dd5a7af7b4b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "dif_airelibre=df.loc[(df.postcovid==1)&(df.airelibre==1)].price.mean()-df.loc[(df.postcovid==0)&(df.airelibre==1)].price.mean()\n",
        "dif_airelibre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11670.161339485458"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHWeVX31a7qh",
        "outputId": "e9cdc7bb-0abb-45a3-89ad-8ecc50b57e78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "dif_sin_airelibre=df.loc[(df.postcovid==1)&(df.airelibre==0)].price.mean()-df.loc[(df.postcovid==0)&(df.airelibre==0)].price.mean()\n",
        "dif_sin_airelibre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8571.756202733664"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_m4oUE1QdKOs",
        "outputId": "e3149dd4-56fc-4593-b895-2c2f60174558",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "dif_airelibre-dif_sin_airelibre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3098.405136751793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbqeUGhDfX-d"
      },
      "source": [
        "Misma cuenta en logs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEB01AYhfc3G",
        "outputId": "635416a2-4c1c-4783-f9a5-2663ae96b7a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "df['logprice']=np.log(df.price)\n",
        "df['logpricem2']=np.log(df.price/df.surface_to)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/geopandas/geodataframe.py:853: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  super(GeoDataFrame, self).__setitem__(key, value)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMIUtWRogKIm",
        "outputId": "6b99dce5-dbce-4578-f2bc-5983beb25da6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "dif_airelibre=df.loc[(df.postcovid==1)&(df.airelibre==1)].logprice.mean()-df.loc[(df.postcovid==0)&(df.airelibre==1)].logprice.mean()\n",
        "dif_sin_airelibre=df.loc[(df.postcovid==1)&(df.airelibre==0)].logprice.mean()-df.loc[(df.postcovid==0)&(df.airelibre==0)].logprice.mean()\n",
        "dif_airelibre-dif_sin_airelibre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017190559076601275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wfm3nFcfhKlc"
      },
      "source": [
        "# Utilizando Regresión\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6fX9nnuhkvI",
        "outputId": "4358f0b5-5145-4ff8-f681-c2bb8c244de1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "df['DD']=df.postcovid*df.airelibre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/geopandas/geodataframe.py:853: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  super(GeoDataFrame, self).__setitem__(key, value)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bRM2eqOhN9p",
        "outputId": "e188fbdb-5d8c-4cb6-f430-a06d88d704d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import statsmodels.api as sm\n",
        "def regress(lista, data):\n",
        "    \"\"\" \n",
        "    Funcion que realiza la regresion OLS\n",
        "    La primera variable de la lista es la dependiente, el resto son las explicativas\n",
        "    data es la base de datos\n",
        "    \"\"\"\n",
        "    datos=data[lista]\n",
        "    # el metodo dropna() me permite eliminar las filas que tienen algun valor missing\n",
        "    datos=datos.dropna()\n",
        "    Y=datos[lista[0]]\n",
        "    X=datos[lista[1:]]\n",
        "    X=sm.add_constant(X)\n",
        "    modelo=sm.OLS(Y,X)\n",
        "    resultados=modelo.fit()\n",
        "    print(resultados.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gw-iVvJNhZ8X",
        "outputId": "cc0267c0-c93c-416e-8c7f-83ecd04e354a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        }
      },
      "source": [
        "regress(['logprice','postcovid','airelibre','DD'],data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:               logprice   R-squared:                       0.038\n",
            "Model:                            OLS   Adj. R-squared:                  0.037\n",
            "Method:                 Least Squares   F-statistic:                     196.4\n",
            "Date:                Thu, 13 Aug 2020   Prob (F-statistic):          5.75e-125\n",
            "Time:                        18:02:36   Log-Likelihood:                -10897.\n",
            "No. Observations:               15099   AIC:                         2.180e+04\n",
            "Df Residuals:                   15095   BIC:                         2.183e+04\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          9.9326      0.005   2087.057      0.000       9.923       9.942\n",
            "postcovid      0.2837      0.017     16.257      0.000       0.249       0.318\n",
            "airelibre      0.1436      0.010     13.951      0.000       0.123       0.164\n",
            "DD             0.0172      0.035      0.486      0.627      -0.052       0.086\n",
            "==============================================================================\n",
            "Omnibus:                     2254.328   Durbin-Watson:                   1.527\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3676.706\n",
            "Skew:                           1.018   Prob(JB):                         0.00\n",
            "Kurtosis:                       4.305   Cond. No.                         9.34\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iq2uTg3ZiGHy",
        "outputId": "27b09623-3a5b-48c0-a51a-3584d83e5dd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        }
      },
      "source": [
        "regress(['price','postcovid','airelibre','DD'],data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.029\n",
            "Model:                            OLS   Adj. R-squared:                  0.029\n",
            "Method:                 Least Squares   F-statistic:                     151.1\n",
            "Date:                Thu, 13 Aug 2020   Prob (F-statistic):           1.75e-96\n",
            "Time:                        18:02:36   Log-Likelihood:            -1.6955e+05\n",
            "No. Observations:               15099   AIC:                         3.391e+05\n",
            "Df Residuals:                   15095   BIC:                         3.391e+05\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       2.371e+04    174.158    136.150      0.000    2.34e+04    2.41e+04\n",
            "postcovid   8571.7562    638.577     13.423      0.000    7320.068    9823.444\n",
            "airelibre   4241.3780    376.554     11.264      0.000    3503.287    4979.469\n",
            "DD          3098.4051   1293.275      2.396      0.017     563.430    5633.380\n",
            "==============================================================================\n",
            "Omnibus:                    11414.989   Durbin-Watson:                   1.624\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           234514.338\n",
            "Skew:                           3.520   Prob(JB):                         0.00\n",
            "Kurtosis:                      20.978   Cond. No.                         9.34\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0gh-cKw_NIA",
        "outputId": "23d08dab-9683-4d90-e1df-32e00d9b90bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.start_date"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2019-07-28'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSAVX3VfEXag"
      },
      "source": [
        "# Inspección gráfica del cumplimiento del supuesto de tendencias comunes\n",
        "\n",
        "Vamos a mirar los promedios mensuales y por trimetre. Para eso voy a extraer de la fecha el mes-anio y el trimestre-anio correspondiente.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kys8h-9I-yir",
        "outputId": "f394d5e5-a704-44b8-b5eb-8b3b0bb282a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "import pandas as pd\n",
        "df['monthyear']=pd.to_datetime(df.start_date).dt.to_period('M')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/geopandas/geodataframe.py:853: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  super(GeoDataFrame, self).__setitem__(key, value)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceo4uqgWAWLV",
        "outputId": "a7f791c3-b6d7-40b6-ec2c-e5fa049e101c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "df_months=df.groupby(by=['monthyear','airelibre']).logprice.mean().rename('media').reset_index()\n",
        "ax=df_months.loc[df_months.airelibre==0].plot(x='monthyear',y='media', label='Sin aire libre')\n",
        "df_months.loc[df_months.airelibre==1].plot(x='monthyear',y='media', ax=ax, label='Aire libre')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e78d2f128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_giZTAc9DmA9",
        "outputId": "f6fdc342-f1aa-49a3-9d71-e2f3c6a584a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "df['quarteryear']=pd.to_datetime(df.start_date).dt.to_period('Q')\n",
        "df_quarters=df.groupby(by=['quarteryear','airelibre']).logprice.mean().rename('media').reset_index()\n",
        "ax=df_quarters.loc[df_quarters.airelibre==0].plot(x='quarteryear',y='media', label='Sin aire libre')\n",
        "df_quarters.loc[df_quarters.airelibre==1].plot(x='quarteryear',y='media', ax=ax, label='Aire libre')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/geopandas/geodataframe.py:853: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  super(GeoDataFrame, self).__setitem__(key, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e78e1ebe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}