20191511_reporte2.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "5dd02de2",
   "metadata": {},
   "source": [
    "                                                     \"REPORTE2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "cde80eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sympy as sy\n",
    "from sympy import *\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5daee0cb",
   "metadata": {},
   "source": [
    "Nombre: Claudia Zevallos \n",
    "\n",
    "Código: 20191511\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525f277a",
   "metadata": {},
   "source": [
    "Respuesta sobre el ensayo:\n",
    "--\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bbd5f5",
   "metadata": {},
   "source": [
    "El presente artículo llamado \"La historía monetaria y fiscal del Perú desde los años 1960 -2017\" del César Martelli de la Universidad George Manson y de Marcos Vega del Banco Central de Reserva del Perú y de la Universidad Pontificie Católica del Perú presenta una análisis de los procesos de económicos fiscales en las últimas décadas teníendo como marco central la inflación, es por ello que llegamos a la conclusión que la pregunta de investigación planteada es: Entre la década de lo 70 y 80 ¿Cuales fueron las causas y consecuencias del aumento de la inflación en esas décadas y como culminó en una hiperinflación o inflación crónica?\n",
    "\n",
    "Dentro del artículo podemos reconocer, desde un análisis al contenido, que está separado por gobierno y espacios temporales que nos brindan un mejor comprensión y concepción de las políticas implementadas en dichos años años junto a sus efectos, además vemos como los autores son conocedores que la materia trabajada. Desde el análisis metodológico y estructural, el audiovisual como los gráficos y las formulas son de muy buena ayuda relacionado con los puntos a trabajar. Por otro lado, referente a las dificultades se puede encontrar que el texto en si es un contenido denso que puede tomar al lector releer varias veces, también en el tema referente de las políticas peruanas monetaria aplicadas, no nos muestra una perspectiva de Perú hacia el exterior que es importante tener presente, pues Perú no es un país independiente en términos económicos.\n",
    "\n",
    "Analizando sobre que casos se pueden añadir otro tema de investigación como lo es la independencia del Banco Central del Perú y la interdependencia económica peruana. Tal como menciona Matari Pierre-Manigat en un artículo llamado \" La independencia del banco central y su papel en el dominio del capital financiero sobre el Estado\" (2019) \"La independencia del banco central designa un estatuto o situación resultante de disposiciones legales y/o de usos y costumbres. Estas reconocen que el banco central no recibe órdenes del gobierno\". Esto nos da a entender que la indepedencia de los bancos y sus funciones no solo debe estar con entidades extranjeras sino los mismo gobiernos nacionales  ya que son una pieza importante para la democracia. \n",
    "\n",
    "\n",
    "Bibliografía:\n",
    "\n",
    "Pierre-Manigat, M. (2019). La independencia del banco central y su papel en el dominio del capital financiero sobre el Estado. Íconos - Revista De Ciencias Sociales, (66), 213–229. https://doi.org/10.17141/iconos.66.2020.3901\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c21090a",
   "metadata": {},
   "source": [
    "Código de Phythom:\n",
    "--\n",
    "\n",
    "Andrea Salas - 20200270\n",
    "    \n",
    "Claudia Zevallos  - 20191511"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a598f8bf",
   "metadata": {},
   "source": [
    "A)El Modelo Ingreso-Gasto: la Curva IS\n",
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2e97a9",
   "metadata": {},
   "source": [
    "-Utilizando las ecuaciones derive paso a paso la curva IS matemáticamente (a partir de la condición de equilibrio Y = DA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d681787f",
   "metadata": {},
   "source": [
    "Para encontrar la Curva de IS, entendemos nuestra fómula de equilibrio como \n",
    "            \n",
    "             Y=DA\n",
    "            \n",
    "En donde conocemos que:\n",
    "\n",
    "             Y= (1/(1-(b-m)(1-t))(Co+Io+Go+Xo+hr)\n",
    "             \n",
    "Ya que estamos analizando desde el análisis parte desde el tasa de interés(r); por lo que se hacen las siguientes operaciones\n",
    "             \n",
    "             r= (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "            \n",
    "Entonces se puede simplicar como: \n",
    "\n",
    "            r= L1/h- (L2/h)y\n",
    "            \n",
    "Donde nuestro intercepto está representado por:\n",
    "\n",
    "            L1: Co+Go+Io+Xo\n",
    "            \n",
    "Y nuestra pendiente está representado por:\n",
    "\n",
    "            L2: 1-(b-m)(1-t)\n",
    "            \n",
    "            \n",
    "                        \n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b1f47d",
   "metadata": {},
   "source": [
    "-Utilizando la función de equilibrio IS donde r está en función de Y, encuentre Δr/ΔY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "0750146e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{\\left(1 - t\\right) \\left(b - m\\right) - 1}{h}$"
      ],
      "text/plain": [
       "((1 - t)*(b - m) - 1)/h"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Primero representamos nuestra ecuación:\n",
    "r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "\n",
    "#Denominamos\n",
    "Co, Io, Go, Xo, Y, b, m, t, h = symbols('Co Io Go Xo Y b m t h')\n",
    "\n",
    "dr_IS_Y = diff(r_IS, Y)\n",
    "dr_IS_Y "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad03fd9",
   "metadata": {},
   "source": [
    "            Δr/ΔY = ((1-t)(b-m)-1)/h\n",
    "             ΔY>0\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f80161",
   "metadata": {},
   "source": [
    "-Lea la sección 4.4 del material de enseñanza y explique cómo se deriva la curva IS a partir del equilibrio  y grafique lo siguiente:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650a7bac",
   "metadata": {},
   "source": [
    "Para encontrar la Curva de IS podemos analizarlo desde el Ingreso y la Demanda agregada que represetan un equilibrio dentro del Mercado de bienes, entendemos nuestra fómula de equilibrio como \n",
    "            \n",
    "                                                   Y=DA\n",
    "            \n",
    "En donde conocemos que:\n",
    "    \n",
    "                                     Y= (1/(1-(b-m)(1-t))(Co+Io+Go+Xo+hr)\n",
    "             \n",
    "Ya que estamos analizando desde el análisis parte desde el tasa de interés; por lo que se hacen las siguientes operaciones\n",
    "\n",
    "             \n",
    "                             r= (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "            \n",
    "\n",
    "Entonces se puede simplicar como: \n",
    "\n",
    "                                                r= L1/h- (L2/h)y\n",
    "            \n",
    "Donde nuestro intercepto está representado por:\n",
    "\n",
    "                                                L1: Co+Go+Io+Xo\n",
    "            \n",
    "Y nuestra pendiente está representado por:\n",
    "\n",
    "                                                L2: 1-(b-m)(1-t)\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6646f281",
   "metadata": {},
   "source": [
    "GRAFICAMOS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "fe66d290",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parámetros\n",
    "\n",
    "Y_size = 100 \n",
    "\n",
    "Co = 35\n",
    "Io = 40\n",
    "Go = 70\n",
    "Xo = 2\n",
    "h = 0.7\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.3\n",
    "r = 0.9\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "\n",
    "def DA_K(Co, Io, Go, Xo, h, r, b, m, t, Y):\n",
    "    DA_K = (Co + Io + Go + Xo - h*r) + ((b - m)*(1 - t)*Y)\n",
    "    return DA_K\n",
    "\n",
    "DA_IS_K = DA_K(Co, Io, Go, Xo, h, r, b, m, t, Y)\n",
    "\n",
    "#--------------------------------------------------\n",
    "# Recta de 45°\n",
    "\n",
    "a = 2.5 \n",
    "\n",
    "def L_45(a, Y):\n",
    "    L_45 = a*Y\n",
    "    return L_45\n",
    "\n",
    "L_45 = L_45(a, Y)\n",
    "\n",
    "#--------------------------------------------------\n",
    "# Segunda curva de ingreso de equilibrio\n",
    "\n",
    "    # Definir cualquier parámetro autónomo\n",
    "Go = 35\n",
    "\n",
    "# Generar la ecuación con el nuevo parámetro\n",
    "def DA_K(Co, Io, Go, Xo, h, r, b, m, t, Y):\n",
    "    DA_K = (Co + Io + Go + Xo - h*r) + ((b - m)*(1 - t)*Y)\n",
    "    return DA_K\n",
    "\n",
    "DA_G = DA_K(Co, Io, Go, Xo, h, r, b, m, t, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "e6edc4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Curva IS\n",
    "Y_size = 100 \n",
    "\n",
    "Co = 35\n",
    "Io = 40\n",
    "Go = 70\n",
    "Xo = 2\n",
    "h = 0.7\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.3\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "# Ecuación \n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "1f613ee3",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 16)) \n",
    "\n",
    "#---------------------------------\n",
    "    # Gráfico 1: ingreso de Equilibrio\n",
    "ax1.yaxis.set_major_locator(plt.NullLocator())   \n",
    "ax1.xaxis.set_major_locator(plt.NullLocator())\n",
    "\n",
    "ax1.plot(Y, DA_IS_K, label = \"DA_0\", color = \"#C6D625\") \n",
    "ax1.plot(Y, DA_G, label = \"DA_1\", color = \"#C6D625\") \n",
    "ax1.plot(Y, L_45, color = \"#D69B25\") \n",
    "\n",
    "ax1.axvline(x = 70.5, ymin= 0, ymax = 0.69, linestyle = \":\", color = \"grey\")\n",
    "ax1.axvline(x = 54,  ymin= 0, ymax = 0.54, linestyle = \":\", color = \"grey\")\n",
    "\n",
    "ax1.text(6, 4, '$45°$', fontsize = 11.5, color = 'black')\n",
    "ax1.text(2.5, -3, '$◝$', fontsize = 30, color = 'black')\n",
    "ax1.text(72, 0, '$Y_0$', fontsize = 12, color = 'black')\n",
    "ax1.text(56, 0, '$Y_1$', fontsize = 12, color = 'black')\n",
    "ax1.text(67, 185, 'E_0', fontsize = 12, color = 'black')\n",
    "ax1.text(50, 142, 'E_1', fontsize = 12, color = 'black')\n",
    "\n",
    "ax1.set(title = \"Curva IS a partir del equilibrio $Y=DA$\", xlabel = r'Y', ylabel = r'DA')\n",
    "ax1.legend()\n",
    "\n",
    "#---------------------------------\n",
    "    # Gráfico 2: Curva IS\n",
    "\n",
    "ax2.yaxis.set_major_locator(plt.NullLocator())   \n",
    "ax2.xaxis.set_major_locator(plt.NullLocator())\n",
    "\n",
    "ax2.plot(Y, r, label = \"IS\", color = \"#FA8072\") \n",
    "\n",
    "ax2.axvline(x = 70.5, ymin= 0, ymax = 1, linestyle = \":\", color = \"grey\")\n",
    "ax2.axvline(x = 54,  ymin= 0, ymax = 1, linestyle = \":\", color = \"grey\")\n",
    "plt.axhline(y = 151.5, xmin= 0, xmax = 0.7, linestyle = \":\", color = \"grey\")\n",
    "plt.axhline(y = 165, xmin= 0, xmax = 0.55, linestyle = \":\", color = \"grey\")\n",
    "\n",
    "ax2.text(72, 128, '$Y_0$', fontsize = 12, color = 'black')\n",
    "ax2.text(56, 128, '$Y_1$', fontsize = 12, color = 'black')\n",
    "ax2.text(1, 153, '$r_0$', fontsize = 12, color = 'black')\n",
    "ax2.text(1, 167, '$r_1$', fontsize = 12, color = 'black')\n",
    "ax2.text(72, 152, 'E_0', fontsize = 12, color = 'black')\n",
    "ax2.text(55, 166, 'E_1', fontsize = 12, color = 'black')\n",
    "\n",
    "ax2.set(title = \"Curva IS\", xlabel = r'Y', ylabel = r'r')\n",
    "\n",
    "ax2.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883f5670",
   "metadata": {},
   "source": [
    "B)La Curva IS o el equilibrio Ahorro- Inversión\n",
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6749b6c0",
   "metadata": {},
   "source": [
    "-Derive la ecuación de IS a partir de la igualdad Ahorro-Inversión y grafique la curva IS de equilibrio en el Mercado de Bienes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd256e38",
   "metadata": {},
   "source": [
    "Entendemos nuestra fómula de equilibrio como \n",
    "            \n",
    "            Y=DA\n",
    "            \n",
    "En donde \n",
    "            \n",
    "            DA= C+I+G+M-X\n",
    "\n",
    "Por lo tanto:\n",
    "\n",
    "            Y=C+I+G+M-X\n",
    "           \n",
    "           \n",
    "Además conocemos que \n",
    "-    T=ty\n",
    "-    y(d)=Y-T\n",
    "- C=Co+by(d)\n",
    "- I=Io-hr\n",
    "- G=Go\n",
    "- X-M= Xo-my(d)\n",
    "\n",
    "Ya que estamos analizando desde el análisis parte desde el tasa de interés; por lo que se hacen las siguientes operaciones\n",
    "\n",
    "            Y-T=C+I+G+X-M-T\n",
    "            \n",
    "            y(d)=C+I+G+X-M-T\n",
    "            \n",
    "Pasamos todo al otro lado\n",
    "\n",
    "            y(d)-C-G-X+M+T = I\n",
    "            \n",
    "            (y(d)-C) + (T-G) + (M-X) = I\n",
    "            \n",
    "            \n",
    "De ahí encontramos tres subdivisiones de ahorro:\n",
    "\n",
    "- El ahorro privado             (y(d)-C)\n",
    "\n",
    "- El ahorro de Gobierno         (T-G)\n",
    "\n",
    "- El ahorro Externo:            (M-X)\n",
    "\n",
    "\n",
    "Entonces la suma de los tres tipos de ahorro (S) es la misma expresion de quilibro con respecto a la inversión: \n",
    "\n",
    "             S=I\n",
    "             \n",
    "Reemplazando econtramos la ecuación para la Curva IS en cuanto a el quilibrio Ahorro= Inversión\n",
    "\n",
    "        (y(d)-C) + (T-G) + (M-X) = I\n",
    "        \n",
    "        (y(d)- Co -by(d)) + (ty-Go) + (my(d)-Xo)= Io - hr\n",
    "        \n",
    "        (1-(b-m)(1-t)Y - (Co + Go + Xo) = Io-hr\n",
    "        \n",
    "        \n",
    "En donde:\n",
    "\n",
    "- La función ahorro está representado por \"(1-(b-m)(1-t)Y - (Co + Go + Xo)\"\n",
    "- La función inversión está representado por  \"I = I0 − hr\"\n",
    "        \n",
    "Siguiendo con la operación de la fórmula derivamos que:\n",
    " \n",
    "                    hr= Io-[1-(b-m)(1-t)Y - (Co+Go+Xo)\n",
    "                    hr= [Io+ (Co+Go+Xo)] - [1-(b-m)(1-t)]Y >0\n",
    "                    r = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "                    \n",
    "En donde nuestro intercepto está representado por:\n",
    "\n",
    "            L1: Io+Co+Go+Xo\n",
    "            \n",
    "Y nuestra pendiente está representado por:\n",
    "\n",
    "            L2: 1-(b-m)(1-t)\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c3ecb454",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "\n",
    "Y_size = 100 \n",
    "\n",
    "Co = 45\n",
    "Io = 50\n",
    "Go = 80\n",
    "Xo = 2\n",
    "h = 0.5\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.3\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "def Ejer4(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    Ejer4 = (1/h)*(Co + Io + Go + Xo) - (1/h)*(1-(b-m)*(1-t))*Y\n",
    "    return Ejer4\n",
    "\n",
    "Ejercicio4 = Ejer4(b, m, t, Co, Io, Go, Xo, h, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "149bda56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos\n",
    "y_max = np.max(Ejercicio4)\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "\n",
    "ax.plot(Y, r, label = \"IS\", color = \"#6C3483\") \n",
    "\n",
    "ax.yaxis.set_major_locator(plt.NullLocator())   \n",
    "ax.xaxis.set_major_locator(plt.NullLocator())\n",
    "\n",
    "ax.set(title = \"Curva de IS\", xlabel= 'Y', ylabel= 'r')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e4e486",
   "metadata": {},
   "source": [
    "C)Desequilibrios en el mercado de bienes\n",
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad0603b",
   "metadata": {},
   "source": [
    "-Grafique las áreas de exceso de oferta o exceso de demanda en el mercado de bienes. Explique por qué estos puntos están fuera de los puntos de equilibrio en el mercado de bienes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5e7be4",
   "metadata": {},
   "source": [
    "El desequilibrio de la en la curva de IS  surge por un exceso de oferta o de demanda que llega a mover la curva de derecha o izquierda respectivamente. En el presente gáfico en donde hay dos ejes: \"r\" y \"y\", en donde se observa tres puntos. El punto A que se encuentra en nuestra curva sería nuestro punto de equilibrio en donde la inversión es igual al ahorro (I = SA) y nuestro escenario perfecto. En el punto B que se puede observar en el lado izquierdo significa el exceso de demanda, en donde se ve que la tasa de interes y la inversión se matiene; sin embargo, el ahorro ha sufrido una caída con lo que representaría un desequilibrio donde la inversión en mayor que el ahorro (I>SB). Por otro lado, en el lado derecho de nuestro punto de equilibrio se encuentra el punto C que representa el exceso de oferta en, de igual manera que la anterior, se mantiene la tasa de interés y de la inversión, pero el ahorro sobrepasa el ahorro en el equilibrio, por lo que el ahorro es mayor que la inversión (I<SC) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6da94aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_size = 100 \n",
    "\n",
    "Co = 35\n",
    "Io = 40\n",
    "Go = 70\n",
    "Xo = 2\n",
    "h = 0.7\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.3\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "\n",
    "def ejer5(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    ejer5 = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return ejer5\n",
    "\n",
    "ejercicio5 = ejer5(b, m, t, Co, Io, Go, Xo, h, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "aa051578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_max = np.max(r)\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "\n",
    "ax.plot(Y, r, label = \"ejercicio5\", color = \"#8B0A50\")\n",
    "\n",
    "\n",
    "ax.yaxis.set_major_locator(plt.NullLocator())   \n",
    "ax.xaxis.set_major_locator(plt.NullLocator())\n",
    "\n",
    "ax.axvline(x = 74, ymin= 0, ymax = 0.45, linestyle = \":\", color = \"grey\")\n",
    "ax.axvline(x = 54,  ymin= 0, ymax = 0.45, linestyle = \":\", color = \"grey\")\n",
    "ax.axvline(x = 34,  ymin= 0, ymax = 0.45, linestyle = \":\", color = \"grey\")\n",
    "plt.axhline(y = 165, xmin= 0, xmax = 1, linestyle = \":\", color = \"grey\")\n",
    "\n",
    "\n",
    "ax.text(74, 165, 'C', fontsize = 20, color = 'black')\n",
    "ax.text(54, 165, 'A', fontsize = 20, color = 'black')\n",
    "ax.text(34, 165, 'B', fontsize = 20, color = 'black')\n",
    "ax.text(1, 165, 'r', fontsize = 16, color = 'black')\n",
    "plt.text(74, 120, '$Y_C$', fontsize = 16, color = 'black')\n",
    "plt.text(54, 120, '$Y_A$', fontsize = 16, color = 'black')\n",
    "plt.text(34, 120, '$Y_B$', fontsize = 16, color = 'black')\n",
    "\n",
    "\n",
    "# Título, ejes y leyenda\n",
    "ax.set(title = \"Desequilibrios en el mercado de bienes\", xlabel= 'Y', ylabel= 'r')\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6bb7f0",
   "metadata": {},
   "source": [
    "D)Movimientos de la curva IS\n",
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fc34dc",
   "metadata": {},
   "source": [
    "-Utilizando el modelo macroeconómico en a) responda los siguiente:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a7d0950",
   "metadata": {},
   "source": [
    "- Analice una Política Fiscal Contractiva con caída del Gasto del Gobierno . El análisis debe ser intuitivo y gráfico."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaa0ecc",
   "metadata": {},
   "source": [
    "Para el análisis intuitivo de una Política Fiscal Contractiva con Caída del Gasto de Gobierno vemos lo siguiente, la caída de Gasto de Gobierno(G) va a significar que la DA baje el motivo es porque son variables que se relación de manera directamente proporciales\n",
    "    \n",
    "         G = Go ↓=DA↓\n",
    "         \n",
    "Entonces el equilibrio se pierde por la disminución de la demanda y Y tiene que bajar para encontrar nuevamente el equilibrio:\n",
    " \n",
    "         DA↓ = DA<Y = Y↓ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "8493794c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parámetros\n",
    "\n",
    "Y_size = 100 \n",
    "\n",
    "Co = 35\n",
    "Io = 40\n",
    "Go = 70\n",
    "Xo = 2\n",
    "h = 0.7\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.3\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "# Ecuación \n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)\n",
    "\n",
    "\n",
    "#--------------------------------------------------\n",
    "    \n",
    "\n",
    "Go = 60\n",
    "\n",
    "\n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r_G = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "e308aa6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Gráfico\n",
    "\n",
    "\n",
    "y_max = np.max(r)\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "\n",
    "ax.plot(Y, r, label = \"IS\", color = \"black\") #IS orginal\n",
    "ax.plot(Y, r_G, label = \"IS_G\", color = \"#DA5E1B\", linestyle = 'dashed') #Nueva IS\n",
    "\n",
    "\n",
    "plt.text(47, 162, '∆Go', fontsize=12, color='black')\n",
    "plt.text(49, 159, '←', fontsize=15, color='grey')\n",
    "\n",
    "\n",
    "ax.set(title = \"Política Fiscal Contractiva con caída del Gasto del Gobiernoo $(G_0)$\", xlabel= 'Y', ylabel= 'r')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd19153b",
   "metadata": {},
   "source": [
    "- Analice una Política Fiscal Expansiva con una caída de la Tasa de Impuestos . El análisis debe ser intuitivo y gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7032e7",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Para el analisis intuitivo de un Política Fiscal Expansiva con una caída de la Tasa de Impuestos ,entendemos que existe dos cambios al modelo de equilibrio\n",
    "En primer lugar, la caída de la tasa de impuestos tiene un efecto inverso en el consumo y en la demanda, en donde:\n",
    "    \n",
    "    t↓ = C↑ = DA↑\n",
    "    \n",
    "Entonces el equilibrio se pierde por el aumento de la demanda y Y tiene que subir para encontrar nuevamente el equilibrio:\n",
    "\n",
    "     DA↑ = DA>Y = Y↑\n",
    "     \n",
    "Por otro lado, la caída de la tasa de impuesto tiene un efecto inverso con respecto a las importanciones y este, por consecuencia, afecta inversamente a la demanda, es por ello que vemos que:\n",
    "\n",
    "     t↓ = M↑ = DA↓\n",
    "\n",
    "Entonces el equilibrio se pierde por la disminución de la demanda y Y tiene que bajar para encontrar nuevamente el equilibrio\n",
    "\n",
    "     DA↓ = DA<Y = Y↓\n",
    " \n",
    "Eso significaría también que el intento de enquilibrio de ambos modelos de cambios son contradictorios\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "664ef63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_size = 100 \n",
    "\n",
    "Co = 35\n",
    "Io = 40\n",
    "Go = 70\n",
    "Xo = 2\n",
    "h = 0.7\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.7\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "# Ecuación \n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)\n",
    "\n",
    "\n",
    "#--------------------------------------------------\n",
    "\n",
    "t = 0.3\n",
    "\n",
    "\n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r_t = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "79feaa1e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "y_max = np.max(r)\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "\n",
    "ax.plot(Y, r, label = \"IS\", color = \"black\") #IS orginal\n",
    "ax.plot(Y, r_t, label = \"IS_t\", color = \"#9863E4\", linestyle = 'dashed') #Nueva IS\n",
    "\n",
    "\n",
    "plt.text(47, 162, '∆t', fontsize=12, color='black')\n",
    "plt.text(47, 158, '->', fontsize=15, color='grey')\n",
    "\n",
    "\n",
    "ax.set(title = \"Política Fiscal Expansiva con una caída de la Tasa de Impuestos $(t)$\", xlabel= 'Y', ylabel= 'r')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c4103c",
   "metadata": {},
   "source": [
    "- Analice una caída de la Propensión Marginal a Consumir . El análisis debe ser intuitivo y gráfico."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d36ecf2",
   "metadata": {},
   "source": [
    "Para el análisis de una política de caída de Propensión Marginal a Consumir(PMC) o (b) en donde afecta  a la Demanda de manera proporcional:\n",
    "\n",
    "        b↓ = C↓ = DA↓\n",
    "        \n",
    "Entonces el equilibrio se pierde por la disminución de la demanda y Y tiene que bajar para encontrar nuevamente el equilibrio\n",
    "\n",
    "        DA↓ = DA<Y = Y↓\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "3698e297",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_size = 100 \n",
    "\n",
    "Co = 35\n",
    "Io = 40\n",
    "Go = 70\n",
    "Xo = 2\n",
    "h = 0.7\n",
    "b = 0.8\n",
    "m = 0.2\n",
    "t = 0.7\n",
    "\n",
    "Y = np.arange(Y_size)\n",
    "\n",
    "\n",
    "# Ecuación \n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)\n",
    "\n",
    "\n",
    "#--------------------------------------------------\n",
    "\n",
    "b = 0.3\n",
    "\n",
    "\n",
    "def r_IS(b, m, t, Co, Io, Go, Xo, h, Y):\n",
    "    r_IS = (Co + Io + Go + Xo - Y * (1-(b-m)*(1-t)))/h\n",
    "    return r_IS\n",
    "\n",
    "r_t = r_IS(b, m, t, Co, Io, Go, Xo, h, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5dc3f299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "y_max = np.max(r)\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "\n",
    "ax.plot(Y, r, label = \"IS\", color = \"black\") #IS orginal\n",
    "ax.plot(Y, r_t, label = \"IS_r\", color = \"#E463B5\", linestyle = 'dashed') #Nueva IS\n",
    "\n",
    "plt.text(47, 162, '∆t', fontsize=12, color='black')\n",
    "plt.text(47, 158, '←', fontsize=15, color='grey')\n",
    "\n",
    "ax.set(title = \"Política de caída de Propensión Marginal $(t)$\", xlabel= 'Y', ylabel= 'r')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
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