Modelo RBC.ipynb

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    "# **Maestria en Ingenieria Economica - Universidad Nacional de Ingenieria**\n",
    "\n",
    "---\n",
    "\n",
    "### Curso: _Macroeconomia_\n",
    "\n",
    "### Profesor (_TA_): _Rodney Menezes_ \n",
    "\n",
    "### Tema: _Modelo RBC Basico (Python)_\n",
    "\n",
    "### Fecha: _07/10/2023_\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27276e0a",
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   "source": [
    "## El Modelo Real Business Cycle (RBC)\n",
    "\n",
    "El modelo Real Business Cycle (RBC) es un modelo macroeconómico que explica las fluctuaciones económicas (ciclos económicos) a través de cambios en la productividad tecnológica. \n",
    "\n",
    "Este modelo se basa en la teoría neoclásica de crecimiento y se caracteriza por el uso de la función de producción Cobb-Douglas, decisiones intertemporales de los agentes y la presencia de choques tecnológicos."
   ]
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   "source": [
    "### Especificaciones del Modelo\n",
    "\n",
    "El modelo que hemos codificado en Python se basa en las siguientes especificaciones:\n",
    "\n",
    "- La función de producción Cobb-Douglas es de la forma $Y_t = Z_t K_t^\\alpha$, donde $Y_t$ es la producción, $K_t$ es el capital, $Z_t$ es el nivel de tecnología, y $\\alpha$ es la elasticidad de la producción respecto al capital.\n",
    "- El modelo utiliza una tasa de depreciación del capital, $\\delta$, y un factor de descuento, $\\beta$.\n",
    "- Los choques tecnológicos siguen un proceso AR(1): $Z_{t+1} = \\rho Z_t + \\epsilon_{t+1}$, donde $\\rho$ es el coeficiente de persistencia y $\\epsilon_{t+1}$ es un término de choque normalmente distribuido.\n"
   ]
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   "source": [
    "### Estado Estacionario \n",
    "\n",
    "El estado estacionario del modelo se caracteriza por los valores de capital, consumo y producción que no cambian con el tiempo. Se calcula de la siguiente manera:\n",
    "\n",
    "$K_{ss} =$ $\\left(\\frac{1 / \\beta - 1 + \\delta}{\\alpha}\\right)^{\\frac{1}{\\alpha - 1}}$\n",
    "\n",
    "$Y_{ss} =$ $Z_{ss} K_{ss}^\\alpha$\n",
    "    \n",
    "$C_{ss} =$ $Y_{ss} - \\delta K_{ss}$\n",
    "    \n",
    "donde $K_{ss}, Y_{ss}, C_{ss}$ y $Z_{ss}$ son los valores de estado estacionario del capital, producción, consumo y tecnología, respectivamente."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aeab6b21",
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    "scrolled": true
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Parámetros del modelo\n",
    "alpha = 0.35  # Elasticidad de la producción respecto al capital\n",
    "beta = 0.96   # Factor de descuento\n",
    "delta = 0.05  # Tasa de depreciación del capital\n",
    "rho = 0.9     # Persistencia del shock tecnológico\n",
    "sigma = 0.02  # Desviación estándar del shock tecnológico\n",
    "\n",
    "# Función de producción Cobb-Douglas\n",
    "def production(k, z):\n",
    "    return z * k**alpha\n",
    "\n",
    "# Función de estado estacionario\n",
    "def steady_state():\n",
    "    k_ss = ((1 / beta - 1 + delta) / alpha)**(1 / (alpha - 1))\n",
    "    z_ss = 1  # Estado estacionario de la tecnología\n",
    "    y_ss = production(k_ss, z_ss)\n",
    "    c_ss = y_ss - delta * k_ss\n",
    "    return k_ss, c_ss, y_ss, z_ss\n",
    "\n",
    "# Shock tecnológico\n",
    "def technology_shock(z_prev, shock=0):\n",
    "    return rho * z_prev + shock\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d1f7f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simular el modelo\n",
    "T = 40  # Periodos de tiempo\n",
    "shock_period = 5  # Periodo en el que ocurre el shock\n",
    "shock_size = 0.1  # Tamaño del shock\n",
    "\n",
    "# Estado inicial\n",
    "k_ss, c_ss, y_ss, z_ss = steady_state()\n",
    "k, z = k_ss, z_ss\n",
    "k_path = [k]\n",
    "c_path = [c_ss]\n",
    "y_path = [y_ss]\n",
    "\n",
    "# Simulación\n",
    "for t in range(1, T):\n",
    "    if t == shock_period:\n",
    "        z = technology_shock(z, shock_size)\n",
    "    else:\n",
    "        z = technology_shock(z)\n",
    "    y = production(k, z)\n",
    "    c = y - delta * k  # Consumo (aproximación simple)\n",
    "    k = beta * alpha * z * k**alpha  # Capital para el próximo periodo (aproximación simple)\n",
    "\n",
    "    k_path.append(k)\n",
    "    c_path.append(c)\n",
    "    y_path.append(y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5651e31",
   "metadata": {},
   "source": [
    "### Simulación y Respuesta a Choques\n",
    "\n",
    "En la simulación, se observa cómo el modelo responde a un choque tecnológico transitorio. \n",
    "\n",
    "El análisis de impulso-respuesta muestra cómo variables clave como el capital, el consumo y la producción responden a lo largo del tiempo después del choque."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f7d8030",
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar Impulso Respuesta\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(k_path, label='Capital')\n",
    "plt.axvline(x=shock_period, color='red', linestyle='--', label='Shock')\n",
    "plt.legend()\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(c_path, label='Consumo')\n",
    "plt.axvline(x=shock_period, color='red', linestyle='--')\n",
    "plt.legend()\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(y_path, label='Producción')\n",
    "plt.axvline(x=shock_period, color='red', linestyle='--')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}