gdp_temp.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shokru/carbon_emissions/blob/main/gdp_temp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "04tL2VGNl0qn"
      },
      "source": [
        "# Economic output and temperature dynamics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "P_dWt7k5ltvh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd                      # This library is for data wrangling\n",
        "import seaborn as sns                    # This is one library for plotting\n",
        "import matplotlib.pyplot as plt          # Yet another library for visualization\n",
        "import plotly.express as px              # This is another library for plots \n",
        "import statsmodels.api as sm             # For the Granger causality test\n",
        "from statsmodels.tsa.stattools import grangercausalitytests\n",
        "import numpy as np\n",
        "pd.set_option('display.max_rows', 6)     # First 3 and last 3 rows will be shown\n",
        "import warnings                          # This is to remove warning to keep output clean\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "z0GvDI76mJUE"
      },
      "source": [
        "## Temperature data\n",
        "\n",
        "There are several sources for global temperatures. All in all, it seems that this does not matter much. As [Wikipedia](https://en.wikipedia.org/wiki/Global_temperature_record) notes: '*Global average temperature datasets from NASA, NOAA, Berkeley Earth, and meteorological offices of the U.K. and Japan, show substantial agreement concerning the progress and extent of global warming: pairwise correlations range from 98.09% to 99.04%.*'\n",
        "\n",
        "First, let's download global annual temperatures from the **NASA** (see the page https://climate.nasa.gov/vital-signs/global-temperature/).  \n",
        "The values related to the difference with **pre-industrial periods**.  \n",
        "This is often taken to be 1850-1900, though it's unclear if this is the base choice in the NASA's data.\n",
        "\n",
        "In full disclosure, we also mention the outstanding [tutorial](https://climateestimate.net/content/getting-started.html) from 4 climate & data researchers. It goes in much more depth and provides code both in R & Python."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "5WVXYUxJl7Bi",
        "outputId": "a71e40e2-2715-4f2c-b1df-f7659d2554b2"
      },
      "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",
              "    }\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>Year</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Smoothing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1880</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1881</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1882</td>\n",
              "      <td>-0.11</td>\n",
              "      <td>-0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2020</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2021</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>2022</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>143 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year  Temperature  Smoothing\n",
              "0    1880        -0.17      -0.10\n",
              "1    1881        -0.09      -0.13\n",
              "2    1882        -0.11      -0.17\n",
              "..    ...          ...        ...\n",
              "140  2020         1.02       0.92\n",
              "141  2021         0.85       0.91\n",
              "142  2022         0.89       0.90\n",
              "\n",
              "[143 rows x 3 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "url = 'https://data.giss.nasa.gov/gistemp/graphs/graph_data/Global_Mean_Estimates_based_on_Land_and_Ocean_Data/graph.txt'\n",
        "data_temp = pd.read_csv(url, skiprows = 4, sep='\\r\\t')  \n",
        "data_temp = data_temp.iloc[0:,0].str.split(expand = True)\n",
        "data_temp = data_temp.rename(columns = {0: 'Year', 1: 'Temperature', 2: 'Smoothing'})\n",
        "data_temp['Temperature'] = pd.to_numeric(data_temp['Temperature']) # Translate to number\n",
        "data_temp['Smoothing'] = pd.to_numeric(data_temp['Smoothing'])     # Translate to number\n",
        "data_temp['Year'] = pd.to_numeric(data_temp['Year'])               # Translate to number\n",
        "data_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "r_y7CV74WoLH",
        "outputId": "3bca0706-95c0-4e2a-f627-82d1ab78b285"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(data_temp.Year, data_temp.Temperature, label = \"Temperature\")\n",
        "plt.plot(data_temp.Year, data_temp.Smoothing, label = \"Smoothed\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.rcParams[\"figure.figsize\"]=(10, 6)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "rgCeT7rNW9g6"
      },
      "source": [
        "The trend post-1960 seems pretty obvious (almost post-1900 if we omit World War II)."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "2QlqhDEvnyAM"
      },
      "source": [
        "Next, GDP data from the **World Bank**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "coFtzDUBn2Wk",
        "outputId": "43b80beb-713c-44bd-e30c-0d2ce4daa30a"
      },
      "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",
              "    }\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>Country</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Indicator Name</th>\n",
              "      <th>Indicator Code</th>\n",
              "      <th>Year</th>\n",
              "      <th>GDP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>1960</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>1961</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>1962</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16223</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>2018</td>\n",
              "      <td>3.415607e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16224</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>2019</td>\n",
              "      <td>2.183223e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16225</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>2020</td>\n",
              "      <td>2.150970e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16226 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Country Country Code     Indicator Name  Indicator Code  Year  \\\n",
              "0         Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1960   \n",
              "1         Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1961   \n",
              "2         Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1962   \n",
              "...         ...          ...                ...             ...   ...   \n",
              "16223  Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2018   \n",
              "16224  Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2019   \n",
              "16225  Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2020   \n",
              "\n",
              "                GDP  \n",
              "0               NaN  \n",
              "1               NaN  \n",
              "2               NaN  \n",
              "...             ...  \n",
              "16223  3.415607e+10  \n",
              "16224  2.183223e+10  \n",
              "16225  2.150970e+10  \n",
              "\n",
              "[16226 rows x 6 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "url = 'https://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.CD?downloadformat=excel'\n",
        "data_gdp = pd.read_excel(url, skiprows = 3) \n",
        "# Putting the data in long format below\n",
        "data_gdp = pd.wide_to_long(data_gdp.iloc[0:,0:65], \n",
        "                stubnames = [''], \n",
        "                i = ['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code'],\n",
        "                j = 'Year').reset_index()\n",
        "data_gdp = data_gdp.rename(columns = {'Country Name': 'Country', '': 'GDP'})\n",
        "data_gdp"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "nmrxjXBfAgyf"
      },
      "source": [
        "Below: a quick look at all **geographical zones** included in the sample: lots of **clusters**!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYuSg4TN-8n1",
        "outputId": "9be04c24-4ed9-493a-9542-056bd88b19fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Aruba', 'Africa Eastern and Southern', 'Afghanistan',\n",
              "       'Africa Western and Central', 'Angola', 'Albania', 'Andorra',\n",
              "       'Arab World', 'United Arab Emirates', 'Argentina', 'Armenia',\n",
              "       'American Samoa', 'Antigua and Barbuda', 'Australia', 'Austria',\n",
              "       'Azerbaijan', 'Burundi', 'Belgium', 'Benin', 'Burkina Faso',\n",
              "       'Bangladesh', 'Bulgaria', 'Bahrain', 'Bahamas, The',\n",
              "       'Bosnia and Herzegovina', 'Belarus', 'Belize', 'Bermuda',\n",
              "       'Bolivia', 'Brazil', 'Barbados', 'Brunei Darussalam', 'Bhutan',\n",
              "       'Botswana', 'Central African Republic', 'Canada',\n",
              "       'Central Europe and the Baltics', 'Switzerland', 'Channel Islands',\n",
              "       'Chile', 'China', \"Cote d'Ivoire\", 'Cameroon', 'Congo, Dem. Rep.',\n",
              "       'Congo, Rep.', 'Colombia', 'Comoros', 'Cabo Verde', 'Costa Rica',\n",
              "       'Caribbean small states', 'Cuba', 'Curacao', 'Cayman Islands',\n",
              "       'Cyprus', 'Czechia', 'Germany', 'Djibouti', 'Dominica', 'Denmark',\n",
              "       'Dominican Republic', 'Algeria',\n",
              "       'East Asia & Pacific (excluding high income)',\n",
              "       'Early-demographic dividend', 'East Asia & Pacific',\n",
              "       'Europe & Central Asia (excluding high income)',\n",
              "       'Europe & Central Asia', 'Ecuador', 'Egypt, Arab Rep.',\n",
              "       'Euro area', 'Eritrea', 'Spain', 'Estonia', 'Ethiopia',\n",
              "       'European Union', 'Fragile and conflict affected situations',\n",
              "       'Finland', 'Fiji', 'France', 'Faroe Islands',\n",
              "       'Micronesia, Fed. Sts.', 'Gabon', 'United Kingdom', 'Georgia',\n",
              "       'Ghana', 'Gibraltar', 'Guinea', 'Gambia, The', 'Guinea-Bissau',\n",
              "       'Equatorial Guinea', 'Greece', 'Grenada', 'Greenland', 'Guatemala',\n",
              "       'Guam', 'Guyana', 'High income', 'Hong Kong SAR, China',\n",
              "       'Honduras', 'Heavily indebted poor countries (HIPC)', 'Croatia',\n",
              "       'Haiti', 'Hungary', 'IBRD only', 'IDA & IBRD total', 'IDA total',\n",
              "       'IDA blend', 'Indonesia', 'IDA only', 'Isle of Man', 'India',\n",
              "       'Not classified', 'Ireland', 'Iran, Islamic Rep.', 'Iraq',\n",
              "       'Iceland', 'Israel', 'Italy', 'Jamaica', 'Jordan', 'Japan',\n",
              "       'Kazakhstan', 'Kenya', 'Kyrgyz Republic', 'Cambodia', 'Kiribati',\n",
              "       'St. Kitts and Nevis', 'Korea, Rep.', 'Kuwait',\n",
              "       'Latin America & Caribbean (excluding high income)', 'Lao PDR',\n",
              "       'Lebanon', 'Liberia', 'Libya', 'St. Lucia',\n",
              "       'Latin America & Caribbean',\n",
              "       'Least developed countries: UN classification', 'Low income',\n",
              "       'Liechtenstein', 'Sri Lanka', 'Lower middle income',\n",
              "       'Low & middle income', 'Lesotho', 'Late-demographic dividend',\n",
              "       'Lithuania', 'Luxembourg', 'Latvia', 'Macao SAR, China',\n",
              "       'St. Martin (French part)', 'Morocco', 'Monaco', 'Moldova',\n",
              "       'Madagascar', 'Maldives', 'Middle East & North Africa', 'Mexico',\n",
              "       'Marshall Islands', 'Middle income', 'North Macedonia', 'Mali',\n",
              "       'Malta', 'Myanmar',\n",
              "       'Middle East & North Africa (excluding high income)', 'Montenegro',\n",
              "       'Mongolia', 'Northern Mariana Islands', 'Mozambique', 'Mauritania',\n",
              "       'Mauritius', 'Malawi', 'Malaysia', 'North America', 'Namibia',\n",
              "       'New Caledonia', 'Niger', 'Nigeria', 'Nicaragua', 'Netherlands',\n",
              "       'Norway', 'Nepal', 'Nauru', 'New Zealand', 'OECD members', 'Oman',\n",
              "       'Other small states', 'Pakistan', 'Panama', 'Peru', 'Philippines',\n",
              "       'Palau', 'Papua New Guinea', 'Poland', 'Pre-demographic dividend',\n",
              "       'Puerto Rico', \"Korea, Dem. People's Rep.\", 'Portugal', 'Paraguay',\n",
              "       'West Bank and Gaza', 'Pacific island small states',\n",
              "       'Post-demographic dividend', 'French Polynesia', 'Qatar',\n",
              "       'Romania', 'Russian Federation', 'Rwanda', 'South Asia',\n",
              "       'Saudi Arabia', 'Sudan', 'Senegal', 'Singapore', 'Solomon Islands',\n",
              "       'Sierra Leone', 'El Salvador', 'San Marino', 'Somalia', 'Serbia',\n",
              "       'Sub-Saharan Africa (excluding high income)', 'South Sudan',\n",
              "       'Sub-Saharan Africa', 'Small states', 'Sao Tome and Principe',\n",
              "       'Suriname', 'Slovak Republic', 'Slovenia', 'Sweden', 'Eswatini',\n",
              "       'Sint Maarten (Dutch part)', 'Seychelles', 'Syrian Arab Republic',\n",
              "       'Turks and Caicos Islands', 'Chad',\n",
              "       'East Asia & Pacific (IDA & IBRD countries)',\n",
              "       'Europe & Central Asia (IDA & IBRD countries)', 'Togo', 'Thailand',\n",
              "       'Tajikistan', 'Turkmenistan',\n",
              "       'Latin America & the Caribbean (IDA & IBRD countries)',\n",
              "       'Timor-Leste', 'Middle East & North Africa (IDA & IBRD countries)',\n",
              "       'Tonga', 'South Asia (IDA & IBRD)',\n",
              "       'Sub-Saharan Africa (IDA & IBRD countries)', 'Trinidad and Tobago',\n",
              "       'Tunisia', 'Turkiye', 'Tuvalu', 'Tanzania', 'Uganda', 'Ukraine',\n",
              "       'Upper middle income', 'Uruguay', 'United States', 'Uzbekistan',\n",
              "       'St. Vincent and the Grenadines', 'Venezuela, RB',\n",
              "       'British Virgin Islands', 'Virgin Islands (U.S.)', 'Vietnam',\n",
              "       'Vanuatu', 'World', 'Samoa', 'Kosovo', 'Yemen, Rep.',\n",
              "       'South Africa', 'Zambia', 'Zimbabwe'], dtype=object)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_gdp.Country.unique()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6ug5ll0b-s8-"
      },
      "source": [
        "Since the temperature (from the NASA data) is measured worldwide, we need to focus only on the **World** subcategory (second-to-last row in the list)."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "TPbJj4LI9aUl"
      },
      "source": [
        "Finally, let's join the two datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ROmM3o2I9eBB",
        "outputId": "e09fc63b-56c6-4eeb-f02d-976c0858fa7c"
      },
      "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",
              "    }\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>Year</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Smoothing</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Indicator Name</th>\n",
              "      <th>Indicator Code</th>\n",
              "      <th>GDP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1960</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>World</td>\n",
              "      <td>WLD</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>1.392273e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1961</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>World</td>\n",
              "      <td>WLD</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>1.448622e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1962</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>World</td>\n",
              "      <td>WLD</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>1.550544e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2018</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.93</td>\n",
              "      <td>World</td>\n",
              "      <td>WLD</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>8.646696e+13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2019</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.92</td>\n",
              "      <td>World</td>\n",
              "      <td>WLD</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>8.765425e+13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2020</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.92</td>\n",
              "      <td>World</td>\n",
              "      <td>WLD</td>\n",
              "      <td>GDP (current US$)</td>\n",
              "      <td>NY.GDP.MKTP.CD</td>\n",
              "      <td>8.511634e+13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Year  Temperature  Smoothing Country Country Code     Indicator Name  \\\n",
              "0   1960        -0.03       0.03   World          WLD  GDP (current US$)   \n",
              "1   1961         0.06       0.01   World          WLD  GDP (current US$)   \n",
              "2   1962         0.03      -0.01   World          WLD  GDP (current US$)   \n",
              "..   ...          ...        ...     ...          ...                ...   \n",
              "58  2018         0.85       0.93   World          WLD  GDP (current US$)   \n",
              "59  2019         0.98       0.92   World          WLD  GDP (current US$)   \n",
              "60  2020         1.02       0.92   World          WLD  GDP (current US$)   \n",
              "\n",
              "    Indicator Code           GDP  \n",
              "0   NY.GDP.MKTP.CD  1.392273e+12  \n",
              "1   NY.GDP.MKTP.CD  1.448622e+12  \n",
              "2   NY.GDP.MKTP.CD  1.550544e+12  \n",
              "..             ...           ...  \n",
              "58  NY.GDP.MKTP.CD  8.646696e+13  \n",
              "59  NY.GDP.MKTP.CD  8.765425e+13  \n",
              "60  NY.GDP.MKTP.CD  8.511634e+13  \n",
              "\n",
              "[61 rows x 8 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_merge = data_temp.merge(data_gdp.query('Country == \"World\"'),\n",
        "      left_on = 'Year', right_on = 'Year')\n",
        "temp_merge"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "8sEVfS8tBeYq"
      },
      "source": [
        "Let's see how the trends compare from 1960 onwards."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "AOUD_RIFBixY",
        "outputId": "84d3da9e-c6a8-4939-f729-d654cfe087d4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = temp_merge.plot(x = \"Year\", y = \"Temperature\", legend = False) \n",
        "plt.plot(data_temp.query('Year > 1959').Year, \n",
        "         data_temp.query('Year > 1959').Smoothing, \n",
        "         label = \"Smoothed\")\n",
        "ax2 = ax.twinx()\n",
        "temp_merge.plot(x = \"Year\", y = \"GDP\", ax = ax2, legend = False, color = \"r\")\n",
        "ax.figure.legend()\n",
        "plt.grid()\n",
        "plt.show()\n",
        "plt.rcParams[\"figure.figsize\"]=(10, 6)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "WU9KGvRK6pKC"
      },
      "source": [
        "## Granger causality\n",
        "### Definition\n",
        "\n",
        "Causality is a complex notion. Clive Granger, who was awarded the Nobel Prize in Economics in 2003, proposed a simple, heuristic model to try to detect a weak form of causality in time-series.   \n",
        "Suppose you are given two time-series, $y_t$ and $x_t$, where $t$ is the time index.  \n",
        "One way to introduce a sense of memory in the model is to assume that $y_t$ depends on past values of the processes, i.e.,  \n",
        "\n",
        "\\begin{align}\n",
        "y_t = a + \\sum_{s=1}^pb_sy_{t-s}+ \\sum_{r=1}^qd_rx_{t-r}+e_t.\n",
        "\\end{align}\n",
        "\n",
        "\n",
        "In the above equation: \n",
        "- $a$ is some real number;\n",
        "- the first sum represents a linear combination of past values of $y_t$, while \n",
        "- the second is a linear combination of the past values of $x_t$;\n",
        "- $e_t$ is an error term: it is not always possible to represent $y_t$ as a simple double sum.   \n",
        "Heuristically, it seems fair to assume that the variable (process) $x$ does not *influence* $y$ if all the coefficents $d_r$ are equal to zero. Basically, this means that $y$ cannot be explained with any past realization of $x_t$.  \n",
        "Thus, we will say that $x$ **does not Granger-cause *y*** if $d_r=0$ for $r=1,...,q$.\n",
        "\n",
        "### Empirical test\n",
        "The test is implemented in the **statsmodels** python module.  \n",
        "It performs 4 similar tests on whether the coefficients $d_r$ of $x$ in the above Equation are all equal to zero.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2S_PCegAh1Y",
        "outputId": "8084e5b6-9ab4-40d6-e12f-9b54373232b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 1\n",
            "ssr based F test:         F=18.4119 , p=0.0001  , df_denom=57, df_num=1\n",
            "ssr based chi2 test:   chi2=19.3809 , p=0.0000  , df=1\n",
            "likelihood ratio test: chi2=16.7948 , p=0.0000  , df=1\n",
            "parameter F test:         F=18.4119 , p=0.0001  , df_denom=57, df_num=1\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 2\n",
            "ssr based F test:         F=7.0920  , p=0.0018  , df_denom=54, df_num=2\n",
            "ssr based chi2 test:   chi2=15.4974 , p=0.0004  , df=2\n",
            "likelihood ratio test: chi2=13.7604 , p=0.0010  , df=2\n",
            "parameter F test:         F=7.0256  , p=0.0019  , df_denom=54, df_num=2\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 3\n",
            "ssr based F test:         F=3.1896  , p=0.0310  , df_denom=53, df_num=3\n",
            "ssr based chi2 test:   chi2=10.4714 , p=0.0150  , df=3\n",
            "likelihood ratio test: chi2=9.6264  , p=0.0220  , df=3\n",
            "parameter F test:         F=3.2859  , p=0.0277  , df_denom=53, df_num=3\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 4\n",
            "ssr based F test:         F=1.5290  , p=0.2077  , df_denom=51, df_num=4\n",
            "ssr based chi2 test:   chi2=6.8357  , p=0.1448  , df=4\n",
            "likelihood ratio test: chi2=6.4559  , p=0.1676  , df=4\n",
            "parameter F test:         F=1.6182  , p=0.1839  , df_denom=51, df_num=4\n"
          ]
        }
      ],
      "source": [
        "granger_test = grangercausalitytests(temp_merge[['Temperature', 'GDP']], maxlag = 4)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "GNgESJoMgDjG"
      },
      "source": [
        "Ok, so how do we interpret these numbers?  \n",
        "In academia, it is customary to focus on the so-called **p-values** (the p=... in the output).    \n",
        "The p-values are a measure of how likely the data is under the **null hypothesis**.  \n",
        "Here, the null is that all coefficients pertaining to $x$ are equal to zero.  \n",
        "When the lag (q) is equal to one, we see that the p-values are very small (0.0000), meaning that the dataset is improbable under the null. Rephrasing it (in a less rigorous way), it means that it's unlikely that the coefficients are equal to zero, and hence that GDP does **not not** cause the temperature series.    \n",
        "When q=4, the p-values are higher (>0.15), and thus the sample is less unlikely. In this case, we cannot reject the null and Granger causality no longer holds.  \n",
        "Naturally, it makes sense that it is harder to unveil causality because it requires ALL coefficients to be zero, which is a stringent assumption.   \n",
        "For the sake of exhaustiveness, it is sometimes customary to perform the test the other way around. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HAhugKwNuKW",
        "outputId": "dde5d150-cdd1-4c47-d7ba-a1d66e71341e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 1\n",
            "ssr based F test:         F=5.9719  , p=0.0177  , df_denom=57, df_num=1\n",
            "ssr based chi2 test:   chi2=6.2862  , p=0.0122  , df=1\n",
            "likelihood ratio test: chi2=5.9782  , p=0.0145  , df=1\n",
            "parameter F test:         F=5.9719  , p=0.0177  , df_denom=57, df_num=1\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 2\n",
            "ssr based F test:         F=3.5552  , p=0.0354  , df_denom=54, df_num=2\n",
            "ssr based chi2 test:   chi2=7.7688  , p=0.0206  , df=2\n",
            "likelihood ratio test: chi2=7.2982  , p=0.0260  , df=2\n",
            "parameter F test:         F=3.5552  , p=0.0354  , df_denom=54, df_num=2\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 3\n",
            "ssr based F test:         F=1.7875  , p=0.1607  , df_denom=53, df_num=3\n",
            "ssr based chi2 test:   chi2=5.8684  , p=0.1182  , df=3\n",
            "likelihood ratio test: chi2=5.5902  , p=0.1333  , df=3\n",
            "parameter F test:         F=1.7875  , p=0.1607  , df_denom=53, df_num=3\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 4\n",
            "ssr based F test:         F=1.4555  , p=0.2295  , df_denom=51, df_num=4\n",
            "ssr based chi2 test:   chi2=6.5069  , p=0.1644  , df=4\n",
            "likelihood ratio test: chi2=6.1616  , p=0.1874  , df=4\n",
            "parameter F test:         F=2.1853  , p=0.0838  , df_denom=51, df_num=4\n"
          ]
        }
      ],
      "source": [
        "granger_test = grangercausalitytests(temp_merge[['GDP', 'Temperature']], maxlag = 4)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "e-WllhHnN_f_"
      },
      "source": [
        "Interestingly, the p-values are larger in this case, meaning that it is more likely that  \n",
        "**GDP => temperature**  \n",
        "than  \n",
        "**temperature => GDP**  \n",
        "(which makes sense, in a way)."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "AYJgqw4iYmA0"
      },
      "source": [
        "## The long term perspective\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "VDJCDRVXY5YZ"
      },
      "source": [
        "The new GDP data is sourced from the Github account of **Our World in Data**, but the origin is the New Maddison Project Database and the World Bank."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "K6OvQ__GY2X8",
        "outputId": "90637d68-d052-4941-c6c0-2691b6031524"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "url =\"https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/World%20GDP%20in%202011%20int%20%24%20%E2%80%93%20OWID%20based%20on%20World%20Bank%20%2B%20Maddison%20(2017)/World%20GDP%20in%202011%20int%20%24%20%E2%80%93%20OWID%20based%20on%20World%20Bank%20%2B%20Maddison%20(2017).csv\"\n",
        "gpd_lt = pd.read_csv(url)\n",
        "gpd_lt.columns.values[2] = \"GDP\"\n",
        "gpd_lt.plot(x = \"Year\", y = \"GDP\");"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OmdPydwYbSWc"
      },
      "source": [
        "A long-term perspective shows the exponential nature of economic growth.  \n",
        "Whether it is sustainable is still the topic of heated debates.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "RCalzNh9gglB",
        "outputId": "1960b0bc-9296-4166-cc1c-80e64c8a74a4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = data_temp.plot(x = \"Year\", y = \"Smoothing\", legend = False, color = 'g') \n",
        "ax2 = ax.twinx()\n",
        "temp_merge.plot(x = \"Year\", y = \"GDP\", ax = ax2, legend = False, color = \"r\")\n",
        "plt.plot(gpd_lt.query('Year > 1849').Year, \n",
        "         gpd_lt.query('Year > 1849').GDP, \n",
        "         label = \"LT GDP\")\n",
        "ax.figure.legend()\n",
        "plt.grid()\n",
        "plt.show()\n",
        "plt.rcParams[\"figure.figsize\"]=(10, 6)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "egO1njkomOuH"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "Economic output and rising temperatures are **linked**. While our analysis is not sufficient to prove **causality**, there is strong consensus among the scientific community that global warming stems from human activity (even via cattle!).  \n",
        "An important note: temperature is not the only relevant indicator. **Precipitation** (& humidity) is another facet of climate that is impacted by GHG emissions. The recent trends show extreme dynamics: dry places become drier and \"wet\" areas experience more and more intense rain episodes."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyPiqSeQ3mHEj8e5TSD1HXll",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}