growth.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMGmCP1yAwt/cFsf2oU2SBr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vatfancio/Teste/blob/main/growth.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Instalando os pacotes que serão utilizados\n",
        "install.packages('pwt10')\n",
        "install.packages('broom')\n",
        "#install.packages('gtsummary')"
      ],
      "metadata": {
        "id": "a9FJWeZfOXqj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1MsARi50Ehin"
      },
      "outputs": [],
      "source": [
        "#Importando os pacotes que serão utilizados.\n",
        "library(pwt10)     #Penn World Table 10.1 (PWT)\n",
        "library(tidyverse) #Manipulação de dados\n",
        "library(broom)\n",
        "#library(gtsummary)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ao importar o pacote 'pw10' com os dados da PWT, as variáveis já são carregadas no data frame chamado 'pwt10.01'.\n",
        "#Como vamos usar o pacote 'dplyr', vamos transformar o data frame em 'tibble', formato familiar para esse pacote do R.\n",
        "\n",
        "df <- as_tibble(pwt10.01)   #Transformando em tibble (formato do dplyr)\n",
        "#glimpse(df)                #Visualizando tipo de cada variável"
      ],
      "metadata": {
        "id": "J0INxAhPEzjm"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A **Penn World Table** é base de dados com informações sobre níveis relativos de rendimento, produção, insumos e produtividade, abrangendo 183 países entre 1950 e 2019.\n",
        "\n",
        "Para mais informações sobre a PWT:\n",
        "https://www.rug.nl/ggdc/productivity/pwt/?lang=en\n",
        "\n",
        "Os detalhes das variáveis da *Penn World Table 10.1* disponíveis nesse pacote do R estão no site:\n",
        "https://cran.r-project.org/web/packages/pwt10/index.html\n",
        "\n",
        "Iremos trabalhar com as seguintes variáveis:\n",
        "\n",
        "* ***ctfp:***  TFP level at current PPPs (USA = 1).\n",
        "* ***delta:*** Average depreciation rate of the capital stock.\n",
        "* ***rgdpo:*** Output-side real GDP at chained PPPs (in million 2017 USD).\n",
        "* ***pop:***   Population (in millions).\n",
        "* ***emp:*** Number of persons engaged (in millions).\n",
        "* ***csh_i:*** Share of gross capital formation at current PPPs.\n",
        "* ***cn:***    Capital stock at current PPPs (in million 2017 USD).\n",
        "\n"
      ],
      "metadata": {
        "id": "YqPwV_MfHllv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Em um modelo com capital humano, temos a seguinte especificação para a Função de Produção:\n",
        "\n",
        "\n",
        "$$Y = K^{\\alpha} (AH)^{1-\\alpha}, \\text{ com } H = e^{\\psi u}L,$$\n",
        "\n",
        "em que $u$ representa a fração de tempo que uma pessoa passa aprendendo novas habilidades.\n",
        "\n",
        "Nesse caso, ela pode ser entendida como anos de escolaridade. Além disso, $\\psi$ indica a elasticidade do capital humano em relação a escolaridade, ou seja, o quanto um ano adicional de estudo aumenta o capital humano.\n"
      ],
      "metadata": {
        "id": "sCBR6cv-Q3MP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "No *Modelo de Solow*, a relação produto-tecnologia no estado estacionário é dada por,\n",
        "\n",
        "$$\\tilde{y}^{*} = \\Bigg(\\dfrac{s_K}{n + g + \\delta}\\Bigg)^{\\frac{\\alpha}{1-\\alpha}}$$\n",
        "\n",
        "Reescrevendo em função do produto por trabalhador,\n",
        "\n",
        "$$y^{*}(t) = \\Bigg(\\dfrac{s_K}{n + g + \\delta}\\Bigg)^{\\frac{\\alpha}{1-\\alpha}}hA(t)$$"
      ],
      "metadata": {
        "id": "4qXDW--rTaXr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ano_inicial <- 1960 #Definindo o *ano inicial* da análise\n",
        "ano_final   <- 2008 #Definindo o *ano final* da análise\n",
        "\n",
        "df <-\n",
        "df |>\n",
        "  filter(year %in% c(ano_inicial,ano_final)) |>                 #Filtrando anos da base\n",
        "  select(country,year,rgdpo,delta,emp,pop,csh_i,cn,ctfp,hc) |>  #Selecionando variáveis de interesse\n",
        "  group_by(country) |>                                          #Agrupando por país\n",
        "  arrange(year, .by_group = T) |>                               #Rearranjando linha do tempo\n",
        "  slice(1,n()) |>                                               #Filtrando ano inicial e final para cada país\n",
        "  mutate(var_year = year - lag(year),                           #Adicionando diferença entre os anos inicial e final\n",
        "         var_year = max(var_year, na.rm = T),\n",
        "\n",
        "         n   = (log(emp)  - lag(log(emp)))/var_year,            #Calculando crescimento do número de trabalhadores. (Checar se não deveria ser crescimento populacional?)\n",
        "         g_a = (log(ctfp) - lag(log(ctfp)))/var_year,           #Calculando crescimento tecnológico\n",
        "\n",
        "         y_p = rgdpo/emp,                                       #Calculando pib per capita\n",
        "\n",
        "         g_y = (log(y_p) - lag(log(y_p)))/var_year,             #Calculando o crescimento do pib per capita\n",
        "\n",
        "         y_p_inicial = lag(y_p)                                 #Pib per capita do período inicial\n",
        "         ) |>\n",
        "  ungroup()"
      ],
      "metadata": {
        "id": "lfKnjKsnXN4o"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Vamos assumir que o crescimento do número de trabalhadores e crescimento da tecnologia é constante ao longo do tempo.\n",
        "#Assim, vamos considerar que o crescimento anual entre 1960 e 2019 é o mesmo que entre 1960 e os anos anteriores.\n",
        "df_inicial <-\n",
        "df |>\n",
        "  group_by(country) |>\n",
        "  mutate(n   = lead(n),\n",
        "         g_a = lead(g_a)) |>\n",
        "  filter(year == ano_inicial) |>\n",
        "  drop_na(y_p,csh_i,g_a,n,delta)"
      ],
      "metadata": {
        "id": "wYnt-RfjLI78"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_an <-      #Definindo a base que será utilizada para análise como 'df_an'\n",
        "  df |>\n",
        "  drop_na()   #Excluindo as variáveis com 'missing'"
      ],
      "metadata": {
        "id": "ho5-oDRKLHBx"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary(df_an)\n",
        "#Pela análise preliminar dos dados, podemos verificar que pelo menos um país apresentou crescimento negativo no período.\n",
        "\n",
        "#Verificando quais países são esses:\n",
        "df_an|>\n",
        "  filter(g_y <= 0)"
      ],
      "metadata": {
        "id": "qIqyZ0IkbqKB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_an |>\n",
        "  #filter(g_y >= -0.04) |>                                       #filtrando outliers\n",
        "  ggplot(aes(x = y_p_inicial, y = g_y)) +                        #definindo variáveis dos eixos x e y\n",
        "  geom_point() +                                                 #definindo tipo de gráfico como 'pontos'\n",
        "  geom_smooth(method = 'lm', se = FALSE) +                       #colocando no gráfico a 'linha da regressão'\n",
        "  ylab(\"Crescimento do PIB por trabalhador entre 1960 e 2019\") + #renomeando texto do eixo y\n",
        "  xlab(\"PIB por trabalhador em 1960\") +                          #renomeando texto do eixo x\n",
        "  theme_bw() +                                                   #definindo estilo do gráfico\n",
        "  theme(axis.title.x = element_text(size = 14),                  #alterando alguns elementos estéticos do gráfico\n",
        "        axis.text.x  = element_text(size = 14),\n",
        "        axis.title.y = element_text(size = 14),\n",
        "        axis.text.y  = element_text(size = 14),\n",
        "        legend.text  = element_text(size = 14),\n",
        "        strip.text.x = element_text(size = 14),\n",
        "        text         = element_text(size = 14))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "KsyeJeZhbSk5",
        "outputId": "99706587-e74c-4aca-8048-0c644edc54bd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
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AAAqqGSUbEBAQGrV692d3ffuHGjZQIB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\njizLXr58mR4XeOfOHSOv1Wg0//nPf3r16uXs7CyXy93d3YcPH3706NHaI0s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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### **Testes para nível de renda per capita**\n",
        "\n",
        "1. Modelo base\n",
        "\n",
        "$$\\ln y_{it} = \\beta_{0} + \\beta_{1} \\ln s_{ki} + \\beta_{2} \\ln(g_A + \\delta + n_i) + \\epsilon_{i}$$\n",
        "\n",
        "\n",
        "2. Modelo aumentado com ***capital humano***\n",
        "\n",
        "$$\\ln y_{it} = \\beta_{0} + \\beta_{1} \\ln s_{ki} + \\beta_{2} \\ln(g_A + \\delta + n_i) + \\beta_{3} \\ln s_{Hi} + \\epsilon_{i}$$\n",
        "\n",
        "em que, $i$ indica o país e $t$ indica o ano.\n"
      ],
      "metadata": {
        "id": "RHs0Rp9eJIT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Modelo base\n",
        "lm(data = df_an,\n",
        "   formula = log(y_p) ~ log(csh_i) + log(g_a + delta + n) ) |>\n",
        "summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "yLWqCYYSSbHh",
        "outputId": "2c6ae3bf-e548-4423-b9a4-992f15459367"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = log(y_p) ~ log(csh_i) + log(g_a + delta + n), data = df_an)\n",
              "\n",
              "Residuals:\n",
              "    Min      1Q  Median      3Q     Max \n",
              "-2.3562 -0.5020  0.1714  0.5314  1.5722 \n",
              "\n",
              "Coefficients:\n",
              "                     Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept)           11.1070     1.2790   8.684 4.42e-12 ***\n",
              "log(csh_i)             1.2971     0.3914   3.314  0.00159 ** \n",
              "log(g_a + delta + n)  -0.4939     0.4294  -1.150  0.25477    \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.8035 on 58 degrees of freedom\n",
              "Multiple R-squared:  0.1699,\tAdjusted R-squared:  0.1413 \n",
              "F-statistic: 5.936 on 2 and 58 DF,  p-value: 0.004513\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos replicar a *Figura 3.8* do Livro do Charles Jones e Dietrich Vollarth (Ver referências no final no arquivo).\n",
        "\n",
        "Nesta figura, temos a relação entre a Taxa de crescimento do PIB por trabalhador entre 1960 e 2008 e o desvio do estado estacionário em 1960. Ela ilustra a afirmação abaixo\n",
        "\n",
        "**Quanto mais \"abaixo\" do seu estado estacionário estiver uma economia, tanto mais rápido deve ser seu crescimento. Quanto mais \"acima\" ela estiver do seu estado estacionário, mais lento será seu crescimento.** (página 60).\n",
        "\n",
        "Essa previsão do modelo neoclássico de crescimento pode ser escrita como,\n",
        "\n",
        "$$\\dfrac{\\dot{\\tilde{k}}}{\\tilde{k}} = s_{k} \\dfrac{\\tilde{y}}{\\tilde{k}} - (n + g + \\delta).$$\n",
        "\n",
        "em que as variáveis têm os seguintes significados: $\\tilde{k}$ a razão capital/tecnologia, $\\tilde{y}$ a razão produto/tecnologia, $s_{k}$ a taxa de poupança, $n$ a taxa de crescimento populacional, $g$ a taxa de crescimento tecnológico, $\\delta$ a depreciação.\n"
      ],
      "metadata": {
        "id": "HN0GD35tOrLm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Para calcular o 'desvio do estado estacionário', vamos considerar o resíduo da regressão do 'Modelo base' para o ano 1960.\n",
        "fit <-\n",
        "lm(data = df_inicial,                                                    #Base considerando o ano inicial\n",
        "   formula = log(y_p) ~ log(csh_i) + log(g_a + delta + n) )              #Regressão modelo base\n",
        "\n",
        "summary(fit)                                                             #Resultado da regressão\n",
        "\n",
        "df_inicial_hat <-\n",
        "df_inicial |>                                                            #Base considerando o ano inicial\n",
        "  bind_cols(augment(fit)) |>                                             #Combinando a base inicial com os resíduos da regressão\n",
        "  select(country,.resid)                                                 #Selecionado as colunas com os nomes dos países e resíduos da regressão\n",
        "\n",
        "df_an |>\n",
        "  #filter(g_y > -0.04)  |>                                               #Filtrando outliers\n",
        "  left_join(df_inicial_hat, by='country') |>\n",
        "  ggplot(aes(x = .resid, y = g_y)) +                                     #Definindo variáveis dos eixos x e y\n",
        "  geom_point() +                                                         #Definindo tipo de gráfico como 'pontos'\n",
        "  geom_smooth(method = 'lm', se = FALSE) +                               #Colocando no gráfico a 'linha da regressão'\n",
        "  ylab(\"Taxa de crescimento do PIB por trabalhador entre 1960 e 2008\") + #Renomeando texto do eixo y\n",
        "  xlab(\"Desvio do Estado Estacionário em 1960\") +                        #Renomeando texto do eixo x\n",
        "  theme_bw() +                                                           #Definindo estilo do gráfico\n",
        "  theme(axis.title.x = element_text(size = 14),                          #Alterando alguns elementos estéticos do gráfico\n",
        "        axis.text.x  = element_text(size = 14),\n",
        "        axis.title.y = element_text(size = 14),\n",
        "        axis.text.y  = element_text(size = 14),\n",
        "        legend.text  = element_text(size = 14),\n",
        "        strip.text.x = element_text(size = 14),\n",
        "        text         = element_text(size = 14))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "Dx_tJn_WOVyL",
        "outputId": "cd5601a9-7f75-4570-c6bc-700c151a256d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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VJVVdXt27dv37596NCh4cOHh4aG2tjY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\n1J+UlBSj0WgymV544QUhRPPmzSv22bVrl06nc3Jyevfdd2VLYWHhn//8ZyF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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Modelo aumentado com capital humano\n",
        "lm(data = df_an,\n",
        "   formula = log(y_p) ~ log(y_p_inicial) + log(csh_i) + log(g_a + delta + n) + log(hc) ) |>\n",
        "  summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "BgdmOUY1fASA",
        "outputId": "f9ae082a-eca5-40d0-f942-1078fca4a9cb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = log(y_p) ~ log(y_p_inicial) + log(csh_i) + log(g_a + \n",
              "    delta + n) + log(hc), data = df_an)\n",
              "\n",
              "Residuals:\n",
              "     Min       1Q   Median       3Q      Max \n",
              "-1.14378 -0.32230  0.03493  0.34279  1.02303 \n",
              "\n",
              "Coefficients:\n",
              "                     Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept)            6.5212     0.9175   7.108 2.30e-09 ***\n",
              "log(y_p_inicial)       0.5762     0.1111   5.186 3.06e-06 ***\n",
              "log(csh_i)             0.7455     0.2377   3.136  0.00273 ** \n",
              "log(g_a + delta + n)   0.7349     0.2973   2.472  0.01651 *  \n",
              "log(hc)                1.7353     0.4015   4.322 6.41e-05 ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.4758 on 56 degrees of freedom\n",
              "Multiple R-squared:  0.7189,\tAdjusted R-squared:  0.6988 \n",
              "F-statistic: 35.81 on 4 and 56 DF,  p-value: 7.805e-15\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Testes de convergência (para taxa de crescimento da renda per capita):**\n",
        "\n",
        "1. Convergência ***incondicional***\n",
        "\n",
        "$$g_{yi}(0,t) = \\beta_{0} + \\beta_{1} y_{i0} + \\epsilon_{i}$$\n",
        "\n",
        "\n",
        "2. Convergência ***condicional*** controlando para capital físico e tx. de crescimento populacional\n",
        "\n",
        "$$g_{yi}(0,t) = \\beta_{0} + \\beta_{1} y_{i0} + \\beta_{2} \\ln s_{ki} + \\beta_{3} \\ln(g_A + \\delta + n_i) + \\epsilon_{i}$$\n",
        "\n",
        "\n",
        "3. Convergência ***condicional*** controlando p/ capital físico, ***capital humano*** e tx. de crescimento populacional\n",
        "\n",
        "$$g_{yi}(0,t) = \\beta_{0} + \\beta_{1} y_{i0} + \\beta_{2} \\ln s_{ki} + \\beta_{3} \\ln(g_A + \\delta + n_i) + \\beta_{4} \\ln s_{Hi} + \\epsilon_{i}$$\n",
        "\n",
        "em que, $i$ indica o país, $t$ indica o ano e *0* o ano inicial."
      ],
      "metadata": {
        "id": "XPocl1hWKFvj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Convergência incondicional\n",
        "lm(data = df_an,\n",
        "   formula = g_y ~ log(y_p_inicial) ) |>\n",
        "  summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "qljFjCY6e2Ln",
        "outputId": "d4038fe9-b25c-4534-b5c4-037ecd732f47"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = g_y ~ log(y_p_inicial), data = df_an)\n",
              "\n",
              "Residuals:\n",
              "      Min        1Q    Median        3Q       Max \n",
              "-0.043989 -0.009032 -0.000128  0.007241  0.031729 \n",
              "\n",
              "Coefficients:\n",
              "                  Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept)       0.071375   0.020008   3.567 0.000723 ***\n",
              "log(y_p_inicial) -0.004939   0.002096  -2.356 0.021813 *  \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.01296 on 59 degrees of freedom\n",
              "Multiple R-squared:  0.08599,\tAdjusted R-squared:  0.0705 \n",
              "F-statistic: 5.551 on 1 and 59 DF,  p-value: 0.02181\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Convergência condicional controlando para capital físico e tx. de crescimento populacional\n",
        "lm(data = df_an,\n",
        "   formula = g_y ~ log(y_p_inicial) + log(csh_i) + log(g_a + delta + n) ) |>\n",
        "  summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "4Wx5qMsEfLXt",
        "outputId": "2b7e7f38-efbf-4bf1-fbf2-8f842311efb3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = g_y ~ log(y_p_inicial) + log(csh_i) + log(g_a + \n",
              "    delta + n), data = df_an)\n",
              "\n",
              "Residuals:\n",
              "       Min         1Q     Median         3Q        Max \n",
              "-0.0210681 -0.0095288 -0.0005449  0.0079565  0.0229655 \n",
              "\n",
              "Coefficients:\n",
              "                      Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept)           0.129598   0.021816   5.940  1.8e-07 ***\n",
              "log(y_p_inicial)     -0.002970   0.002147  -1.383  0.17202    \n",
              "log(csh_i)            0.017404   0.005647   3.082  0.00317 ** \n",
              "log(g_a + delta + n)  0.019104   0.007018   2.722  0.00858 ** \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.01135 on 57 degrees of freedom\n",
              "Multiple R-squared:  0.3236,\tAdjusted R-squared:  0.288 \n",
              "F-statistic:  9.09 on 3 and 57 DF,  p-value: 5.2e-05\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Convergência condicional controlando p/ capital físico, capital humano e tx. de crescimento populacional\n",
        "lm(data = df_an,\n",
        "   formula = g_y ~ log(y_p_inicial) + log(csh_i) + log(g_a + delta + n) + log(hc) ) |>\n",
        "  summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "1SIKEJk_fOPN",
        "outputId": "665cc54f-ba95-4d8d-ec28-3f024565d5f6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = g_y ~ log(y_p_inicial) + log(csh_i) + log(g_a + \n",
              "    delta + n) + log(hc), data = df_an)\n",
              "\n",
              "Residuals:\n",
              "       Min         1Q     Median         3Q        Max \n",
              "-0.0238287 -0.0067145  0.0007278  0.0071415  0.0213131 \n",
              "\n",
              "Coefficients:\n",
              "                      Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept)           0.135859   0.019115   7.108 2.30e-09 ***\n",
              "log(y_p_inicial)     -0.008830   0.002315  -3.815 0.000342 ***\n",
              "log(csh_i)            0.015532   0.004952   3.136 0.002727 ** \n",
              "log(g_a + delta + n)  0.015309   0.006193   2.472 0.016506 *  \n",
              "log(hc)               0.036153   0.008365   4.322 6.41e-05 ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.009913 on 56 degrees of freedom\n",
              "Multiple R-squared:  0.4928,\tAdjusted R-squared:  0.4565 \n",
              "F-statistic:  13.6 on 4 and 56 DF,  p-value: 8.237e-08\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Referências\n",
        "\n",
        "1. Charles Jones e Dietrich Vollarth. Introdução à Teoria do Crescimento Econômico. 3. ed - Rio de Janeiro. Elsevier, 2015.\n",
        "\n",
        "2. Pablo Kurlat. A Course in Modern Macroeconomics.\n",
        "\n",
        "    *Link:* https://sites.google.com/view/pkurlat/a-course-in-modern-macroeconomics?authuser=0\n",
        "\n",
        "3. Julieta Caunedo. Development accounting: Methods and findings.\n",
        "\n",
        "    *Link*: https://www.youtube.com/watch?v=lG-DvqsxVfg&list=PLt9jC4Ylzr6BuFVwZNW0hCJureBVMBJ-Z&index=2"
      ],
      "metadata": {
        "id": "lbHM-nO-NJ7K"
      }
    }
  ]
}