is713_group2.ipynb

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      "name": "is713_group2.ipynb",
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/pansilup/is713_grp2/blob/master/is713_group2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTlOtAYIWAqo"
      },
      "source": [
        "# IS 713 Group 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onkYivdRYUWx"
      },
      "source": [
        "## 1. Introduction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kBEzAqmJM20F",
        "outputId": "aca569ae-da1a-4154-973a-58092b3cc63c"
      },
      "source": [
        "# add the EDA of the data\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "\n",
        "wb = gc.open_by_url('https://docs.google.com/spreadsheets/d/1oQQAU542s8wPHNJNmNBICaMDh75AiXBVPJagODpWz4Q/edit#gid=1250765839')\n",
        "sheet = wb.worksheet('final_dataset')\n",
        "data = sheet.get_all_values()\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "df = pd.DataFrame(data)\n",
        "df.columns = df.iloc[0]\n",
        "df = df.iloc[1:]\n",
        "# df[\"year\"] = pd.to_numeric(df[\"year\"])\n",
        "df.replace('', np.nan, inplace=True)\n",
        "df.dropna(inplace=True)\n",
        "df.reset_index(drop=True,inplace=True)\n",
        "\n",
        "df['consumer_price_index'] = df['consumer_price_index'].astype('float32')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>no</th>\n",
              "      <th>country</th>\n",
              "      <th>year</th>\n",
              "      <th>fertility_rate</th>\n",
              "      <th>consumer_price_index</th>\n",
              "      <th>female-labourFrc-percentage</th>\n",
              "      <th>dragon_year</th>\n",
              "      <th>tiger_year</th>\n",
              "      <th>chinese_prominance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1990</td>\n",
              "      <td>1.83</td>\n",
              "      <td>71.816734</td>\n",
              "      <td>37.5420903</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1991</td>\n",
              "      <td>1.73</td>\n",
              "      <td>74.276962</td>\n",
              "      <td>37.6152636</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1992</td>\n",
              "      <td>1.72</td>\n",
              "      <td>75.957901</td>\n",
              "      <td>37.732357</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1993</td>\n",
              "      <td>1.74</td>\n",
              "      <td>77.696808</td>\n",
              "      <td>38.0359161</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1994</td>\n",
              "      <td>1.71</td>\n",
              "      <td>80.105507</td>\n",
              "      <td>38.1750817</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2015</td>\n",
              "      <td>2.198</td>\n",
              "      <td>129.322678</td>\n",
              "      <td>41.6164699</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2016</td>\n",
              "      <td>2.182</td>\n",
              "      <td>138.283218</td>\n",
              "      <td>41.100912</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.168</td>\n",
              "      <td>144.606262</td>\n",
              "      <td>40.5913159</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2018</td>\n",
              "      <td>2.154</td>\n",
              "      <td>154.544083</td>\n",
              "      <td>41.0267118</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2019</td>\n",
              "      <td>2.138</td>\n",
              "      <td>168.182693</td>\n",
              "      <td>40.221261</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>270 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "0   no    country  year  ... dragon_year  tiger_year chinese_prominance\n",
              "0    1  Singapore  1990  ...           0           0                  1\n",
              "1    1  Singapore  1991  ...           0           0                  1\n",
              "2    1  Singapore  1992  ...           0           0                  1\n",
              "3    1  Singapore  1993  ...           0           0                  1\n",
              "4    1  Singapore  1994  ...           0           0                  1\n",
              "..  ..        ...   ...  ...         ...         ...                ...\n",
              "265  9    Myanmar  2015  ...           0           0                  0\n",
              "266  9    Myanmar  2016  ...           0           0                  0\n",
              "267  9    Myanmar  2017  ...           0           0                  0\n",
              "268  9    Myanmar  2018  ...           0           0                  0\n",
              "269  9    Myanmar  2019  ...           0           0                  0\n",
              "\n",
              "[270 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KhK3Af1M8u6"
      },
      "source": [
        "!pip install --upgrade plotly\n",
        "!pip install jupyter-dash\n",
        "\n",
        "import plotly\n",
        "plotly.__version__"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "x71SiqPGiN_n",
        "outputId": "a0bee052-b1db-4728-9772-30b7e529f82e"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.line(df, x=\"year\", y=\"fertility_rate\", color=\"country\")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "zHun0DcljL5y",
        "outputId": "c1af8d73-181a-4729-f447-4df83e25bf86"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.line(df, x=\"year\", y=\"consumer_price_index\", color=\"country\")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "eDlpCy9Lfqjl",
        "outputId": "f5796b60-dfc9-44d1-afb3-1829da12d5be"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.line(df, x=\"year\", y=\"consumer_price_index\", color=\"chinese_prominance\", facet_col=\"country\")\n",
        "                  #hover_data=['female-labourFrc-percentage'])\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "7KIibfC4koCP"
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "d4KrjU4zPPN3",
        "outputId": "5a12f023-b242-4a5a-a0c6-5e9b7a601056"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(df, x=\"year\", y=\"fertility_rate\", color=\"country\", size='consumer_price_index',)\n",
        "                  #hover_data=['female-labourFrc-percentage'])\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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    {
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      "metadata": {
        "colab": {
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          "height": 542
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        "id": "gBmi7nHyOtgT",
        "outputId": "a0d4771a-9f68-4577-e4b2-24cb3ba0d7e3"
      },
      "source": [
        "import plotly.express as px\n",
        "df_sg = df[df['country']=='Singapore']\n",
        "fig = px.scatter(df_sg, x=\"chinese_x_dragon\", y=\"fertility_rate\", color=\"country\",)\n",
        "                 #size='female-labourFrc-percentage', hover_data=['female-labourFrc-percentage'])\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"chinese_x_dragon\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"fertility_rate\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
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              "var x = new MutationObserver(function (mutations, observer) {{\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTGBQ6onYYG4"
      },
      "source": [
        "## 2. Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51Z-hLF5ZK0h"
      },
      "source": [
        "# Use standard errors that are clustered by country\n",
        "# experiment with time and entity fixed effects\n",
        "\n",
        "#%%Model_1\n",
        "#          Fertility_rate_it = A ‘Country-with-chinese-culture_i’ x ‘Dragon-year_t’ +  C ‘consumer-price-index_it’ + err\n",
        "\n",
        "# #%%Model_2\n",
        "#           Fertility_rate_it = B ‘Country-with-chinese-culture_i’ x Tiger-year_t’ + C ‘consumer-price-index_it’ + err\n",
        "\n",
        "# #%%Model_3\n",
        "#           Fertility_rate_it = A ‘Country-with-chinese-culture_i’ x ‘Dragon-year_t’ + B ‘Country-with-chinese-culture_i’ x Tiger-year_t’ + C ‘consumer-price-index_it’ + err"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGe1ZjLmWxiv"
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "\n",
        "wb = gc.open_by_url('https://docs.google.com/spreadsheets/d/1oQQAU542s8wPHNJNmNBICaMDh75AiXBVPJagODpWz4Q/edit#gid=1250765839')\n",
        "sheet = wb.worksheet('final_dataset')\n",
        "data = sheet.get_all_values()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "oUnbMYBDW_30",
        "outputId": "2679ac58-07df-453e-ab65-53a20b45394b"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "df = pd.DataFrame(data)\n",
        "df.columns = df.iloc[0]\n",
        "df = df.iloc[1:]\n",
        "# df[\"year\"] = pd.to_numeric(df[\"year\"])\n",
        "df.replace('', np.nan, inplace=True)\n",
        "df.dropna(inplace=True)\n",
        "df.reset_index(drop=True,inplace=True)\n",
        "df "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>no</th>\n",
              "      <th>country</th>\n",
              "      <th>year</th>\n",
              "      <th>fertility_rate</th>\n",
              "      <th>consumer_price_index</th>\n",
              "      <th>female-labourFrc-percentage</th>\n",
              "      <th>dragon_year</th>\n",
              "      <th>tiger_year</th>\n",
              "      <th>chinese_prominance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1990</td>\n",
              "      <td>1.83</td>\n",
              "      <td>71.8167331</td>\n",
              "      <td>37.5420903</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1991</td>\n",
              "      <td>1.73</td>\n",
              "      <td>74.2769602</td>\n",
              "      <td>37.6152636</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1992</td>\n",
              "      <td>1.72</td>\n",
              "      <td>75.9579006</td>\n",
              "      <td>37.732357</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1993</td>\n",
              "      <td>1.74</td>\n",
              "      <td>77.6968046</td>\n",
              "      <td>38.0359161</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1994</td>\n",
              "      <td>1.71</td>\n",
              "      <td>80.1055086</td>\n",
              "      <td>38.1750817</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2015</td>\n",
              "      <td>2.198</td>\n",
              "      <td>129.322672</td>\n",
              "      <td>41.6164699</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2016</td>\n",
              "      <td>2.182</td>\n",
              "      <td>138.283214</td>\n",
              "      <td>41.100912</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.168</td>\n",
              "      <td>144.606264</td>\n",
              "      <td>40.5913159</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2018</td>\n",
              "      <td>2.154</td>\n",
              "      <td>154.544082</td>\n",
              "      <td>41.0267118</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>9</td>\n",
              "      <td>Myanmar</td>\n",
              "      <td>2019</td>\n",
              "      <td>2.138</td>\n",
              "      <td>168.182701</td>\n",
              "      <td>40.221261</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>270 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "0   no    country  year  ... dragon_year tiger_year chinese_prominance\n",
              "0    1  Singapore  1990  ...           0          0                  1\n",
              "1    1  Singapore  1991  ...           0          0                  1\n",
              "2    1  Singapore  1992  ...           0          0                  1\n",
              "3    1  Singapore  1993  ...           0          0                  1\n",
              "4    1  Singapore  1994  ...           0          0                  1\n",
              "..  ..        ...   ...  ...         ...        ...                ...\n",
              "265  9    Myanmar  2015  ...           0          0                  0\n",
              "266  9    Myanmar  2016  ...           0          0                  0\n",
              "267  9    Myanmar  2017  ...           0          0                  0\n",
              "268  9    Myanmar  2018  ...           0          0                  0\n",
              "269  9    Myanmar  2019  ...           0          0                  0\n",
              "\n",
              "[270 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0OG7OwK6VUX"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#%% PP\n",
        "## Final models start here ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXCjYBQkCfRK"
      },
      "source": [
        "!pip install linearmodels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJDSwNWcX9it"
      },
      "source": [
        "import pandas as pd\n",
        "import statsmodels.formula.api as sm\n",
        "from linearmodels import PanelOLS\n",
        "\n",
        "exceldata = pd.ExcelFile('sample_data/final3.xlsx')\n",
        "df = exceldata.parse('final_dataset')\n",
        "#print(df)\n",
        "\n",
        "df['chinese_x_dragon'] = df['chinese_prominance']*df['dragon_year']\n",
        "df['chinese_x_tiger'] = df['chinese_prominance']*df['tiger_year']\n",
        "df3 = df.set_index(['country', 'year'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfSNRyETX-BB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLFXF3BqVUBe"
      },
      "source": [
        "## Model 1\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwwXJhOYiYd-"
      },
      "source": [
        "model_1_0 = PanelOLS.from_formula('fertility_rate ~ consumer_price_index+EntityEffects',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_1_0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hS1pY2GCLnVU"
      },
      "source": [
        "model_1_1 = PanelOLS.from_formula('fertility_rate ~ dragon_year+tiger_year+consumer_price_index+EntityEffects',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_1_1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jra7cmOWX-e5"
      },
      "source": [
        "model_1_2 = PanelOLS.from_formula('fertility_rate ~ consumer_price_index+dragon_year+tiger_year+chinese_x_tiger+chinese_x_dragon',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_1_2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-ZWnANPnrD_"
      },
      "source": [
        "## Model 2\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WqVttn6ikm0"
      },
      "source": [
        "model_2_0 = PanelOLS.from_formula('change_in_fertility_rate_subtrt ~ change_in_cons_prc_idx_subtrt+EntityEffects',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_2_0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmiIeHq-ST-G"
      },
      "source": [
        "model_2_1 = PanelOLS.from_formula('change_in_fertility_rate_subtrt ~ change_in_cons_prc_idx_subtrt+dragon_year+tiger_year+EntityEffects',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_2_1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEzk_E2W6PYQ"
      },
      "source": [
        "model_2_2 = PanelOLS.from_formula('change_in_fertility_rate_subtrt ~ change_in_cons_prc_idx_subtrt+dragon_year+tiger_year+chinese_x_dragon+chinese_x_tiger+EntityEffects',data=df3,drop_absorbed=True).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_2_2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJf1E3DZnxjr"
      },
      "source": [
        "## Model 3\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXTP1sPUiwFG"
      },
      "source": [
        "model_3_0 = PanelOLS.from_formula('change_in_fertility_rate_mvg_avg ~ change_in_cons_prc_idx_mvg_avg+EntityEffects',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_3_0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aONRH9eldGh3"
      },
      "source": [
        "model_3_1 = PanelOLS.from_formula('change_in_fertility_rate_mvg_avg ~ change_in_cons_prc_idx_mvg_avg+dragon_year+tiger_year+EntityEffects',data=df3).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_3_1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfVM-4bjdIv0"
      },
      "source": [
        "model_3_2 = PanelOLS.from_formula('change_in_fertility_rate_mvg_avg ~ change_in_cons_prc_idx_mvg_avg+dragon_year+tiger_year+chinese_x_dragon+chinese_x_tiger+EntityEffects',data=df3,drop_absorbed=True).fit(cov_type='clustered', cluster_entity=True, cluster_time=True)\n",
        "print(model_3_2)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}