{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "836ca90d", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:23.083810Z", "start_time": "2024-08-21T02:06:23.081525Z" } }, "outputs": [], "source": [ "from cge_modeling import Variable, Parameter, Equation, cge_model\n", "from cge_modeling import plotting as cgp\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "a3e1e660", "metadata": {}, "source": [ "# Modeling with `cge_modeling`\n", "\n", "A CGE model is comprised of three components:\n", "\n", "1. Variables: symbolic objects that represent model quantities that will vary endogenously in your model.\n", "2. Parameters: symbolic objects that represent model quantities that will vary exogenously in your model (or not at all!)\n", "3. Equations: A mathematical description of your model economy.\n", "\n", "To create a model, one first must write down all the variables and parameters that will go into the model." ] }, { "cell_type": "markdown", "id": "a8b26908", "metadata": {}, "source": [ "## Model Setup\n", "\n", "In this first simple model, we consider an economy with a single household and a single firm. The household has fixed endowments of labor and capital, which it sells to the firm for wages and rents, respectively. The firm uses these to produce a single consumption good, which it then sells back to the household. There is no government and no investment. The structure of the economy is shown in the following graph:" ] }, { "cell_type": "code", "execution_count": 2, "id": "b6781767", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:23.430145Z", "start_time": "2024-08-21T02:06:23.202817Z" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "Household\n", "\n", "Household\n", "\n", "\n", "\n", "K_s\n", "\n", "K_s\n", "\n", "\n", "\n", "Household->K_s\n", "\n", "\n", "\n", "\n", "\n", "L_s\n", "\n", "L_s\n", "\n", "\n", "\n", "Household->L_s\n", "\n", "\n", "\n", "\n", "\n", "C\n", "\n", "C\n", "\n", "\n", "\n", "Household->C\n", "\n", "\n", "\n", "\n", "\n", "Capital Market\n", "\n", "Capital Market\n", "\n", "\n", "\n", "K_s->Capital Market\n", "\n", "\n", "r\n", "\n", "\n", "\n", "Labor Market\n", "\n", "Labor Market\n", "\n", "\n", "\n", "L_s->Labor Market\n", "\n", "\n", "w\n", "\n", "\n", "\n", "Goods Market\n", "\n", "Goods Market\n", "\n", "\n", "\n", "C->Goods Market\n", "\n", "\n", "P\n", "\n", "\n", "\n", "Firm\n", "\n", "Firm\n", "\n", "\n", "\n", "Y\n", "\n", "Y\n", "\n", "\n", "\n", "Firm->Y\n", "\n", "\n", "\n", "\n", "\n", "K_d\n", "\n", "K_d\n", "\n", "\n", "\n", "Firm->K_d\n", "\n", "\n", "\n", "\n", "\n", "L_d\n", "\n", "L_d\n", "\n", "\n", "\n", "Firm->L_d\n", "\n", "\n", "\n", "\n", "\n", "Y->Goods Market\n", "\n", "\n", "P\n", "\n", "\n", "\n", "K_d->Capital Market\n", "\n", "\n", "r\n", "\n", "\n", "\n", "L_d->Labor Market\n", "\n", "\n", "w\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import graphviz as gr\n", "\n", "\n", "def draw_graph(edge_list, node_props=None, edge_props=None, graph_direction=\"UD\"):\n", " \"\"\"Utility to draw a causal (directed) graph\"\"\"\n", " g = gr.Digraph(\n", " graph_attr={\"rankdir\": graph_direction, \"ratio\": \"0.25\"},\n", " engine=\"neato\",\n", " )\n", "\n", " edge_props = {} if edge_props is None else edge_props\n", " for e in edge_list:\n", " props = edge_props[e] if e in edge_props else {}\n", " g.edge(e[0], e[1], **props)\n", "\n", " if node_props is not None:\n", " for name, props in node_props.items():\n", " g.node(name=name, **props)\n", " return g\n", "\n", "\n", "nodes = [\"K\", \"L\", \"Y\", \"C\", \"Household\", \"Firm\"]\n", "edges = [\n", " (\"Household\", \"K_s\"),\n", " (\"Household\", \"L_s\"),\n", " (\"Household\", \"C\"),\n", " (\"Firm\", \"Y\"),\n", " (\"Firm\", \"K_d\"),\n", " (\"Firm\", \"L_d\"),\n", " (\"K_d\", \"Capital Market\"),\n", " (\"L_d\", \"Labor Market\"),\n", " (\"K_s\", \"Capital Market\"),\n", " (\"L_s\", \"Labor Market\"),\n", " (\"C\", \"Goods Market\"),\n", " (\"Y\", \"Goods Market\"),\n", "]\n", "edge_props = {\n", " (\"K_d\", \"Capital Market\"): {\"label\": \"r\"},\n", " (\"K_s\", \"Capital Market\"): {\"label\": \"r\"},\n", " (\"L_d\", \"Labor Market\"): {\"label\": \"w\"},\n", " (\"L_s\", \"Labor Market\"): {\"label\": \"w\"},\n", " (\"C\", \"Goods Market\"): {\"label\": \"P\"},\n", " (\"Y\", \"Goods Market\"): {\"label\": \"P\"},\n", "}\n", "draw_graph(edges, edge_props=edge_props, graph_direction=\"LR\")" ] }, { "cell_type": "markdown", "id": "93d4100b", "metadata": {}, "source": [ "Given this structure, the economy will be comprised of the following equations:\n", "\n", "$$\n", "\\begin{align}\n", " Y &= C & \\text{Goods market clearing} \\\\\n", " K_d &= K_s & \\text{Capital market clearing} \\\\\n", " L_d &= L_s & \\text{Labor market clearing} \\\\\n", " Y &= f(K_d, L_d) & \\text{Production function} \\\\\n", " \\frac{r}{P} &= \\frac{\\partial f \\left (K_d, L_d \\right )}{\\partial K_d} & \\text{Firm demand for capital} \\\\\n", " \\frac{w}{P} &= \\frac{\\partial f \\left (K_d, L_d \\right )}{\\partial L_d} & \\text{Firm demand for labor} \\\\\n", " PC &= rK_s + wL_s + \\Pi & \\text{Household budget constraint}\n", "\\end{align}\n", "$$\n", "\n", "Since we are an endowment economy, we take $K_s$ and $L_s$ as given -- they are thus parameters. The variables are $Y$, $C$, $K_d$, $L_d$, $w$, $r$, and $P$. $\\Pi$, the profits of the firm, we set to zero to eliminate. So we have 7 variables and 7 unknowns." ] }, { "cell_type": "markdown", "id": "e1d711fc", "metadata": {}, "source": [ "### Underdetermination\n", "\n", "The system above looks square, but actually it's not! The household budget constraint is equivalent to the goods market clearning condition. To see this, you have to know that the equations for the firm's factor demand are based on the zero profit condtion. Suppose we don't eliminate $\\Pi$, and instead use the identity:\n", "\n", "$$\\Pi = P Y - r K_d - w L_d$$\n", "\n", "Substituting this into the household budget constraint, we obtain:\n", "\n", "$$PC = r K_s + w L_s + P Y - r K_d - w L_d $$\n", "\n", "From the factor clearing conditions we have $K_d = K_s$ and $L_d = L_s$, so we can make these substitutions:\n", "\n", "$$\\begin{align}\n", "PC &= r K_s - r K_s + w L_s - w L_s + PY \\\\\n", "PC &= PY \\\\\n", "C &= Y\n", "\\end{align}\n", "$$\n", "\n", "We recovered the market clearing condition from the other equations! This shows that although we wrote down 7 equations, we actually only have 6 equations worth of information." ] }, { "cell_type": "markdown", "id": "f5a0caf1", "metadata": {}, "source": [ "### Walras' Law and Choice of Numeraire\n", "\n", "It's surprising that we only have 6 independent equations, given that we have 7 variables. But actually we only have 6 variables. This is because of Walras' law. The law says that, given perfect competiton and flexible prices in all markets (which we've assumed), the sum of the values of excess demands (or supplies) must be zero. This is an additional constraint on the economy, which has the effect of pinning down the price of the last good, assuming we know the prices of all other goods. In our case, if we know values of $r$ and $w$, we are *not* free to choose a value for $P$. \n", "\n", "As a result, one of our prices is not actually a variable at all. We are required to choose a numeraire -- a price that all other prices are measured by. The choice of numeraire is the modeler's. I will choose $w$, the wage level, to be the numeraire. As a result, all prices will be expressed in hourly wages.\n", "\n", "Conceptually, we think of $w$ as a parameter now (which we will fix to be 1 -- since any numeraire value implies a valid price vector, we might as well choose the easiest possible value). Now we have 6 variables are 7 equations, 6 of which are independent. " ] }, { "cell_type": "markdown", "id": "9ab5df37", "metadata": {}, "source": [ "### Squaring up the system\n", "\n", "Now we just need to make the system square. We have two choices. First, we could remove one of the redundant equations (either the household budget constraint or the goods market clearing conditions). Alternatively, we can add a dummy \"residual\" variable, which we set to zero. This second choice is popular because it adds a consistency check to model simulations. This residual should always be zero, at all valid equlibria. \n", "\n", "We will add a residual term, $\\varepsilon$, to the market clearing condition. So our system now becomes:" ] }, { "cell_type": "markdown", "id": "edc0a944", "metadata": {}, "source": [ "### The Final System" ] }, { "cell_type": "markdown", "id": "8b6f956b", "metadata": {}, "source": [ "Finally, we need to specify a production function. We can use Cobb-Douglas, so $Y = f(K_d, L_d) = AK_d^\\alpha L_d^{1-\\alpha}$.\n", "\n", "The system then becomes:\n", "\n", "$$\n", "\\begin{align}\n", " Y &= C + \\varepsilon & \\text{Goods market clearing} \\\\\n", " K_d &= K_s & \\text{Capital market clearing} \\\\\n", " L_d &= L_s & \\text{Labor market clearing} \\\\\n", " Y &= AK_d^\\alpha L_d^{1-\\alpha} & \\text{Production function} \\\\\n", " K_d &= \\alpha Y \\frac{P}{r} & \\text{Firm demand for capital} \\\\\n", " L_d &= (1 - \\alpha) Y \\frac{P}{w} & \\text{Firm demand for labor} \\\\\n", " PC &= rK_s + wL_s + \\Pi & \\text{Household budget constraint}\n", "\\end{align}\n", "$$\n", "\n", "Where $Y$, $C$, $K_d$, $L_d$, $r$, $P$, and $\\varepsilon$ are variables. $A$, $\\alpha$, $L_s$, $K_s$, and $w$ are parameters.\n", "\n", "We have 7 variables and 7 independent equations (since the market clearing and budget constraint now vary by $\\varepsilon$!), and we are ready to proceed. " ] }, { "cell_type": "markdown", "id": "88e472ea", "metadata": {}, "source": [ "## Declare Variables\n", "\n", "First, we make a list of variables using the `Variable` class. In their simplest form, variables have a `name` and a `description`. We can optionally also specify a `latex_name` for pretty printing." ] }, { "cell_type": "code", "execution_count": 3, "id": "cb03f51b", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:23.434771Z", "start_time": "2024-08-21T02:06:23.431965Z" } }, "outputs": [], "source": [ "variables = [\n", " Variable(\"Y\", description=\"Total output\"),\n", " Variable(\"C\", description=\"Household constumption\"),\n", " Variable(\"K_d\", description=\"Firm demand for capital\"),\n", " Variable(\"L_d\", description=\"Firm demand for labor\"),\n", " Variable(\"r\", description=\"Rental rate of capital\"),\n", " Variable(\"P\", description=\"Price of consumption goods\"),\n", " Variable(\"resid\", latex_name=\"\\\\varepsilon\", description=\"Walrasian residual\"),\n", "]" ] }, { "cell_type": "markdown", "id": "3123d41f", "metadata": {}, "source": [ "## Declare Parameters\n", "\n", "`Parameter`s are exactly the same as `Variable`s, except that they are treated differently inside the model." ] }, { "cell_type": "code", "execution_count": 4, "id": "a316d325", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:23.437582Z", "start_time": "2024-08-21T02:06:23.435495Z" } }, "outputs": [], "source": [ "parameters = [\n", " Parameter(\"A\", description=\"Total factor productivity\"),\n", " Parameter(\"alpha\", description=\"Share of capital in production\"),\n", " Parameter(\"L_s\", description=\"Household endowment of labor\"),\n", " Parameter(\"K_s\", description=\"Household endowment of capital\"),\n", " Parameter(\"w\", description=\"Wage level\"),\n", "]" ] }, { "cell_type": "markdown", "id": "44d9df30", "metadata": {}, "source": [ "## Declare equations\n", "\n", "`Equation`s also have a name, but instead of a description, you need to provide a string form of the equation. " ] }, { "cell_type": "code", "execution_count": 5, "id": "4bf6d331", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:23.441384Z", "start_time": "2024-08-21T02:06:23.439174Z" } }, "outputs": [], "source": [ "equations = [\n", " Equation(\"Production function\", \"Y = A * K_d ** alpha * L_d ** (1 - alpha)\"),\n", " Equation(\"Firm capital demand function\", \"K_d = alpha * Y * P / r\"),\n", " Equation(\"Firm labor demand function\", \"L_d = (1 - alpha) * Y * P / w\"),\n", " Equation(\"Household budget constraint\", \"P * C = r * K_s + w * L_s\"),\n", " Equation(\"Labor market clearing\", \"L_s = L_d\"),\n", " Equation(\"Capital market clearing\", \"K_s = K_d\"),\n", " Equation(\"Goods market clearing\", \"Y = C + resid\"),\n", "]" ] }, { "cell_type": "markdown", "id": "0b5d6a17", "metadata": {}, "source": [ "# Create a model\n", "\n", "With these these lists, we are ready to create a CGEModel" ] }, { "cell_type": "code", "execution_count": 6, "id": "dab3cada", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.821533Z", "start_time": "2024-08-21T02:06:23.442188Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initial pre-processing complete, found 7 equations, 7 variables, 5 parameters\n" ] } ], "source": [ "mod = cge_model(variables=variables, parameters=parameters, equations=equations)" ] }, { "cell_type": "markdown", "id": "54772097", "metadata": {}, "source": [ "Once the model is built, we can look at the equations" ] }, { "cell_type": "code", "execution_count": 7, "id": "c355dc90", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.825798Z", "start_time": "2024-08-21T02:06:25.822223Z" }, "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\n", "\t\n", "\t\t\\begin{array}{|c|c|c|}\n", "\t\t\t\\hline\n", "\t\t\t\\textbf{} & \\textbf{Name} & \\textbf{Equation} \\\\\n", "\t\t\t\\hline\n", "\t\t\t1 & \\text{Production function} & Y = A K_{d}^{\\alpha} L_{d}^{1 - \\alpha} \\\\\n", "\t\t\t\\hline\n", "\t\t\t2 & \\text{Firm capital demand function} & K_{d} = \\frac{P Y \\alpha}{r} \\\\\n", "\t\t\t\\hline\n", "\t\t\t3 & \\text{Firm labor demand function} & L_{d} = \\frac{P Y \\left(1 - \\alpha\\right)}{w} \\\\\n", "\t\t\t\\hline\n", "\t\t\t4 & \\text{Household budget constraint} & C P = K_{s} r + L_{s} w \\\\\n", "\t\t\t\\hline\n", "\t\t\t5 & \\text{Labor market clearing} & L_{s} = L_{d} \\\\\n", "\t\t\t\\hline\n", "\t\t\t6 & \\text{Capital market clearing} & K_{s} = K_{d} \\\\\n", "\t\t\t\\hline\n", "\t\t\t7 & \\text{Goods market clearing} & Y = C + \\varepsilon \\\\\n", "\t\t\t\\hline\n", "\t\t\\end{array}\n", "\t\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mod.equation_table()" ] }, { "cell_type": "markdown", "id": "2983539a", "metadata": {}, "source": [ "As well as the parameters and variables" ] }, { "cell_type": "code", "execution_count": 8, "id": "ea5c4cf4", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.829382Z", "start_time": "2024-08-21T02:06:25.826649Z" } }, "outputs": [ { "data": { "text/latex": [ "\n", "\t\n", "\t\t\\begin{array}{|c|c|}\n", "\t\t\t\\hline\n", "\t\t\t\\textbf{Symbol} & \\textbf{Description} \\\\\n", "\t\t\t\\hline\n", "\t\t\tY & \\text{Total output} \\\\\n", "\t\t\t\\hline\n", "\t\t\tC & \\text{Household constumption} \\\\\n", "\t\t\t\\hline\n", "\t\t\tK_d & \\text{Firm demand for capital} \\\\\n", "\t\t\t\\hline\n", "\t\t\tL_d & \\text{Firm demand for labor} \\\\\n", "\t\t\t\\hline\n", "\t\t\tr & \\text{Rental rate of capital} \\\\\n", "\t\t\t\\hline\n", "\t\t\tP & \\text{Price of consumption goods} \\\\\n", "\t\t\t\\hline\n", "\t\t\t\\varepsilon & \\text{Walrasian residual} \\\\\n", "\t\t\t\\hline\n", "\t\t\\end{array}\n", "\t\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mod.summary(\"variables\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "b797b7fd", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.832364Z", "start_time": "2024-08-21T02:06:25.830111Z" } }, "outputs": [ { "data": { "text/latex": [ "\n", "\t\n", "\t\t\\begin{array}{|c|c|}\n", "\t\t\t\\hline\n", "\t\t\t\\textbf{Symbol} & \\textbf{Description} \\\\\n", "\t\t\t\\hline\n", "\t\t\tA & \\text{Total factor productivity} \\\\\n", "\t\t\t\\hline\n", "\t\t\t\\alpha & \\text{Share of capital in production} \\\\\n", "\t\t\t\\hline\n", "\t\t\tL_s & \\text{Household endowment of labor} \\\\\n", "\t\t\t\\hline\n", "\t\t\tK_s & \\text{Household endowment of capital} \\\\\n", "\t\t\t\\hline\n", "\t\t\tw & \\text{Wage level} \\\\\n", "\t\t\t\\hline\n", "\t\t\\end{array}\n", "\t\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mod.summary(\"parameters\")" ] }, { "cell_type": "markdown", "id": "acbf9b92", "metadata": {}, "source": [ "Or, if you prefer, everything together" ] }, { "cell_type": "code", "execution_count": 10, "id": "ec1cf066", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.835307Z", "start_time": "2024-08-21T02:06:25.832874Z" } }, "outputs": [ { "data": { "text/latex": [ "\n", "\t\n", "\t\t\\begin{array}{|c|c|}\n", "\t\t\t\\hline\n", "\t\t\t\\textbf{Symbol} & \\textbf{Description} \\\\\n", "\t\t\t\\hline\n", "\t\t\tY & \\text{Total output} \\\\\n", "\t\t\t\\hline\n", "\t\t\tC & \\text{Household constumption} \\\\\n", "\t\t\t\\hline\n", "\t\t\tK_d & \\text{Firm demand for capital} \\\\\n", "\t\t\t\\hline\n", "\t\t\tL_d & \\text{Firm demand for labor} \\\\\n", "\t\t\t\\hline\n", "\t\t\tr & \\text{Rental rate of capital} \\\\\n", "\t\t\t\\hline\n", "\t\t\tP & \\text{Price of consumption goods} \\\\\n", "\t\t\t\\hline\n", "\t\t\t\\varepsilon & \\text{Walrasian residual} \\\\\n", "\t\t\t\\hline\n", "\t\t\tA & \\text{Total factor productivity} \\\\\n", "\t\t\t\\hline\n", "\t\t\t\\alpha & \\text{Share of capital in production} \\\\\n", "\t\t\t\\hline\n", "\t\t\tL_s & \\text{Household endowment of labor} \\\\\n", "\t\t\t\\hline\n", "\t\t\tK_s & \\text{Household endowment of capital} \\\\\n", "\t\t\t\\hline\n", "\t\t\tw & \\text{Wage level} \\\\\n", "\t\t\t\\hline\n", "\t\t\\end{array}\n", "\t\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mod.summary()" ] }, { "cell_type": "markdown", "id": "2736135f", "metadata": {}, "source": [ "# Data and Calibration\n", "\n", "To actually run policy experiments, you have to first find a non-trivial model equlibrium. To check for an equlibrium, you can use the `model.check_for_equlibrium` method. It expects a dictionary containing all model variables and parameters, and checks whether all provided model equations are equal to zero" ] }, { "cell_type": "code", "execution_count": 11, "id": "73290c01", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.841132Z", "start_time": "2024-08-21T02:06:25.837189Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Equilibrium not found. Total squared error: 34.004656\n", "\n", "\n", "Equation Residual\n", "====================================================================================================\n", "Production function -0.084686\n", "Firm capital demand function 1.923461\n", "Firm labor demand function 0.022343\n", "Household budget constraint -3.172644\n", "Labor market clearing 4.451211\n", "Capital market clearing -0.481453\n", "Goods market clearing -0.431901\n" ] } ], "source": [ "all_objects = mod.variable_names + mod.parameter_names\n", "random_initial_point = dict(zip(all_objects, np.random.normal(size=len(all_objects)) ** 2))\n", "mod.check_for_equilibrium(random_initial_point)" ] }, { "cell_type": "markdown", "id": "fc0ce160", "metadata": {}, "source": [ "For data, we will use a (fake!) balanced SAM. We exploit the fact that the SAM is balanced to \"calibrate\" the model, choosing parameter values that lead to the data observed in the SAM. Calibration is unique to each model, so `cge_modeling` currently can't do that for you. What it can do is help you with some consistency checks. We need to:\n", "\n", "1. Write a python function that takes the model, plus all required initial data from the SAM\n", "2. Inside the function, make sure every parameter and variable is given a value\n", "3. return `model.check_calibration()`\n", "\n", "`model.check_calibration()` specifically looks at the local namespace, so it's important that it is called from within a function." ] }, { "cell_type": "markdown", "id": "f8fecd84", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "code", "execution_count": 12, "id": "fc3681af", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.850588Z", "start_time": "2024-08-21T02:06:25.841811Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Factor Institution Production Activities\n", " Labor Capital Household Firm Firm\n", "Factor Labor 0.0 0.0 0.0 0.0 7000.0\n", " Capital 0.0 0.0 0.0 0.0 3000.0\n", "Institution Household 7000.0 3000.0 0.0 0.0 0.0\n", "Production Firm 0.0 0.0 10000.0 0.0 0.0\n", "Activities Firm 0.0 0.0 0.0 10000.0 0.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "sam = pd.read_csv(\"../data/simple_rbc_data.csv\", index_col=[0, 1], header=[0, 1]).fillna(0)\n", "sam" ] }, { "cell_type": "markdown", "id": "e3838c7c", "metadata": {}, "source": [ "First we can double check that this is a properly balanced SAM by checking that the sum of the rows is equal to the sum of the columns" ] }, { "cell_type": "code", "execution_count": 13, "id": "3a736c35", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.854599Z", "start_time": "2024-08-21T02:06:25.851372Z" } }, "outputs": [ { "data": { "text/plain": [ "Factor Labor True\n", " Capital True\n", "Institution Household True\n", "Production Firm True\n", "Activities Firm True\n", "dtype: bool" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sam.sum(axis=1) == sam.sum(axis=0)" ] }, { "cell_type": "markdown", "id": "3f4a8456", "metadata": {}, "source": [ "For bigger SAMs its not realistic to look row by row, so we can use `.all()`" ] }, { "cell_type": "code", "execution_count": 14, "id": "d16237d5", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.858312Z", "start_time": "2024-08-21T02:06:25.855470Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(sam.sum(axis=1) == sam.sum(axis=0)).all()" ] }, { "cell_type": "markdown", "id": "602ab636", "metadata": {}, "source": [ "Next, we extract the required initial values. In this case we actually know basically everything, but let's try to work with the minimum set: $Y$, $K_d$, $L_d$." ] }, { "cell_type": "code", "execution_count": 15, "id": "551e64e4", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.861424Z", "start_time": "2024-08-21T02:06:25.858974Z" } }, "outputs": [], "source": [ "initial_values = {\n", " \"Y\": sam.loc[(\"Activities\", \"Firm\"), (\"Production\", \"Firm\")],\n", " \"K_d\": sam.loc[(\"Factor\", \"Capital\"), (\"Activities\", \"Firm\")],\n", " \"L_d\": sam.loc[(\"Factor\", \"Labor\"), (\"Activities\", \"Firm\")],\n", "}" ] }, { "cell_type": "code", "execution_count": 16, "id": "89edfef3", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.864445Z", "start_time": "2024-08-21T02:06:25.862242Z" } }, "outputs": [ { "data": { "text/plain": [ "{'Y': 10000.0, 'K_d': 3000.0, 'L_d': 7000.0}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_values" ] }, { "cell_type": "markdown", "id": "9762c374", "metadata": {}, "source": [ "## Calibration\n", "\n", "Now that we have data, we need to \"calibrate\" the CGE model to the data. Calibration is process in which we use observed values in the same to calculate parameter values. All widely used production and utility functions have off-the-sheft calibration equations for all or most of the parameters. In the case of the Cobb-Douglas production function, the calibrating equations are:\n", "\n", "$$\\begin{align} \n", "\\alpha &= \\frac{r K_d}{r K_d + w L_d} \\\\\n", "A &= \\frac{Y}{K_d ^\\alpha L_d ^ {1 - \\alpha}}\n", "\\end{align}$$\n", "\n", "It is easy to see that the second equation is just a re-arrangement of the production function, conditional on knowing the value of $\\alpha$. The equation for $\\alpha$ itself comes from \"inverting\" the factor demand functions:\n", "\n", "$$\\begin{align}\n", "K_d &= \\alpha Y \\frac{P}{r} & \\to Y P &= \\frac{K_d r}{\\alpha} \\\\\n", "L_d &= (1 - \\alpha) Y \\frac{P}{w} & \\to Y P &= \\frac{L_d w}{1 - \\alpha} \\\\ \n", "\\frac{K_d r}{\\alpha} &= \\frac{L_d w}{1 - \\alpha} \\\\\n", "\\frac{1 - \\alpha}{\\alpha} &= \\frac{L_d w}{K_d r} \\\\\n", "\\frac{1}{\\alpha} - 1 &= \\frac{L_d w}{K_d r} \\\\\n", "\\frac{1}{\\alpha} &= \\frac{L_d w}{K_d r} + \\frac{K_d r}{K_d r} \\\\\n", "\\alpha &= \\frac{K_d r}{K_d r + L_d w}\n", "\\end{align}$$" ] }, { "cell_type": "markdown", "id": "757308a8", "metadata": {}, "source": [ "### A sidenote on prices\n", "\n", "To perform this calibration, we need to know $K_d$, $L_d$ , and $Y$. It is tempting to read these values off from the SAM. After all, it records transfers from the activity account (the firms' purchases of inputs) to the factor accounts (e.g. $K_d$ and $L_d$) as well as flows from the production account to the activity account (the firm's output). Recall though that the SAM records the *value* of these flows -- that is, $\\text{Price} \\times \\text{Quantity}$. \n", "\n", "When computing $\\alpha$ the situation isn't so bad, because all terms enter as values. That is, we have $rK_d$ and $wL_d$, and never $K_d$ or $L_d$ (the quantity variables) alone. But for computing $A$, we do need to pick apart the price and quantity. Since we're unlikely to have access to detailed price data, trickery will be employed.\n", "\n", "The trick in this case is called \"normalization\". To understand the trick, first realize that \"quantity\" is a value with units. We might not know what these units are without digging into statistical documentation, but the SAM might record, for example, purchases of wheat by households from wheat farmers in bushels. So the value $V = PQ$ in this case would be $\\text{Currency} \\times \\text{Bushel}$. \n", "\n", "Note that this choice of unit is entirely arbitrary. If we knew the price of a kilogram of wheat instead, we could just as easily have reported $V = P^\\prime Q^\\prime$, with $P^\\prime Q^\\prime$ measured in $\\text{Currency} \\times \\text{Kilograms}$. Importantly, though, the total value would be the same -- note that I did not write $V^\\prime$ in the 2nd equation; this was delibrate!\n", "\n", "Given this, there is a sense in which we are free to choose whatever units we wish, as long as we are consistent. Let us choose the most convenient unit possible -- the \"Unit Currency Unit\". That is, we choose whatever unit of every good, such that the price of that good is exactly 1 unit of currency. Labor is measured in whatever fraction of time such that the wage for that time is one dollar. Cars are measured in fractional cars, such that the price of the fraction is one dollar, and so on. \n", "\n", "This might strike you as a bit silly, but you have to admit it's very convenient! We are now free to set $P=1$, and $r=1$ (recall that we already set $w=1$, since it was chosen to be the numeraire). What changes is our interprertation. We cannot report \"natural\" units on the outputs of our simulations, since everything will be measured in \"Unit Currency Units\". But not all hope is lost, because percentages are unitless! So we can calibrate to this odd unit, then report the results of a policy experiment in percentages. " ] }, { "cell_type": "markdown", "id": "24c272f2", "metadata": {}, "source": [ "## Implementing the calibration\n", "\n", "Now we know how to actually do the calibration. We will:\n", "\n", "1. Read in the necessary values from the SAM, in this case $Y$, $K_d$, and $L_d$.\n", "2. Normalize all prices to 1.\n", "3. Calibrate $\\alpha$ and $A$ using the formulas derived above\n", "4. Compute all variables using model equations\n", "\n", "`cge_modeling` provides a few tools to help you accomplish this. First, when setting constant values, you can use `model.initialize_parameter`. This will take care of shapes automatically for models with many dimensions. Second, you can use `model.finalize_calibration`. This will automatically collect the variables in a function context, check shapes, and return a dictionary of results. If anything was not declared, it will give a reminder.\n", "\n", "The following cell shows how to use this features to calibrate our model. `resid` has been purposely omitted, to show how `finalize_calibration` will alert us to missing variables or parameters" ] }, { "cell_type": "code", "execution_count": 17, "id": "08ebb64f", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.867365Z", "start_time": "2024-08-21T02:06:25.865242Z" } }, "outputs": [], "source": [ "def calibrate_model(mod, *, Y, K_d, L_d):\n", " # Normalize prices\n", " r = mod.initialize_parameter(\"r\", 1.0)\n", " w = mod.initialize_parameter(\"w\", 1.0)\n", " P = mod.initialize_parameter(\"P\", 1.0)\n", "\n", " # Calibrate parameters\n", " alpha = r * K_d / (r * K_d + w * L_d)\n", " A = Y / K_d**alpha / L_d ** (1 - alpha)\n", "\n", " # Market clearing conditions\n", " K_s = K_d\n", " L_s = L_d\n", " C = Y\n", "\n", " return mod.finalize_calibration()" ] }, { "cell_type": "code", "execution_count": 18, "id": "697da516", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.869866Z", "start_time": "2024-08-21T02:06:25.868137Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "resid not found in model variables\n" ] } ], "source": [ "calibrated_values = calibrate_model(mod, **initial_values)" ] }, { "cell_type": "markdown", "id": "56f59a08", "metadata": {}, "source": [ "Oops, we forgot `resid`. Let's go back and fix it:" ] }, { "cell_type": "code", "execution_count": 19, "id": "d212759e", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.872770Z", "start_time": "2024-08-21T02:06:25.870564Z" } }, "outputs": [], "source": [ "def calibrate_model(mod, *, Y, K_d, L_d):\n", " # Normalize prices\n", " r = mod.initialize_parameter(\"r\", 1.0)\n", " w = mod.initialize_parameter(\"w\", 1.0)\n", " P = mod.initialize_parameter(\"P\", 1.0)\n", "\n", " # Calibrate parameters\n", " alpha = r * K_d / (r * K_d + w * L_d)\n", " A = Y / K_d**alpha / L_d ** (1 - alpha)\n", "\n", " # Market clearing conditions\n", " K_s = K_d\n", " L_s = L_d\n", " C = Y\n", " resid = Y - C\n", "\n", " return mod.finalize_calibration()" ] }, { "cell_type": "markdown", "id": "ebed3b47", "metadata": {}, "source": [ "In Python, no news is good news! " ] }, { "cell_type": "code", "execution_count": 20, "id": "be24d013", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.882995Z", "start_time": "2024-08-21T02:06:25.874403Z" } }, "outputs": [], "source": [ "calibrated_values = calibrate_model(mod, **initial_values)" ] }, { "cell_type": "markdown", "id": "9db2a555", "metadata": {}, "source": [ "We can check that the calibrated values have zero residual when plugged into the model equations " ] }, { "cell_type": "code", "execution_count": 21, "id": "7ffd31ed", "metadata": { "ExecuteTime": { "end_time": "2024-08-21T02:06:25.888426Z", "start_time": "2024-08-21T02:06:25.884051Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Equilibrium found! Total squared error: 0.000000\n" ] } ], "source": [ "mod.check_for_equilibrium(calibrated_values)" ] }, { "cell_type": "markdown", "id": "20664bc5", "metadata": {}, "source": [ "We have an equlibrium. We can now proceed to policy simulation" ] }, { "cell_type": "markdown", "id": "468eb9a9", "metadata": {}, "source": [ "# Policy Simulation\n", "\n", "To perform a policy simulation, use the `simulate` method. This method has a number of options, but the most important are the following:\n", "\n", "- `initial_state` is the initial equilibrium from which to begin. In most cases this will be the calibrated equlibrium, but it could also be a previous simulation (if you're doing recursive modeling through time, for example)\n", "\n", "- `final_delta`, `final_delta_pct`, or `final_values` are how you specify the policy simulation. Ultimately, what you need to provide is a vector of final parameter values -- the policy to be simulated. Each of these options are available to help you better reason about that. Values passed to `final_delta` are added to the inital parameter values; values passed to `final_delt_pct` are multiplied by the initial values; and `final_values` completely override the initial values.\n", "\n", " Suppose the initial value of labor supply was 1000, and we want to simulate a cut in labor supply to 500. The following specifications are all equivalent:\n", " \n", " 1. `final_values={'L_s':500}`\n", " 2. `final_delta={'L_s':-500}`\n", " 3. `final_delta_pct = {'L_s':0.5}`\n", " \n", "- `use_euler_approximation` and `use_optimizer`. By default, `cge_modeling` solves for the policy solution in two ways:\n", "\n", " 1. Linearize the model, and convert the policy shock into an implicit ODE. This is \"euler approximation\". Given infinite compute, it always gives the right answer, but involves some expensive matrix inversion. As a bonus, it gives you all equlibria on the way from the initial state to the final state, so you can see a small slice of the function space.\n", " \n", " 2. Solve for the new equlibrium using a numerical routine. This can be more direct, but sometimes struggles on complex problems.\n", " \n", " The strategy of `cge_modeling` is to use the euler approximation with a conservative number of steps to move the model into the neighborhood of the solution, then use a numerical optimizer to finish the job. You acn disable one or the other, though, if you don't want to use both.\n", " \n", "- `optimizer_mode`, one of `root` or `minimize`. `root` solves for a vector of zeros directly, using multivariate root finding algorithms. `minimize` tries to minimize the sum of squared residuals using some flavor of Newton's Method. Usually `root` works fine, but on really stubbon problems `minimize` is nice to try.\n", "\n", "\n", "Other options are related to saving and loading model results. Any other keyword arguments are passed to the `scipy` optimizer being used, so check the scipy documentation to learn what you can do there. " ] }, { "cell_type": "markdown", "id": "cfdf81dd", "metadata": {}, "source": [ "## 50% Labor Supply Reduction\n", "\n", "We now (finally) run a policy simulation, to study what happens if households work 50% less. I use the `final_delta_pct` argument to directly specify the cut in percentage terms." ] }, { "cell_type": "code", "execution_count": 22, "id": "83721f93", "metadata": { "ExecuteTime": { "start_time": "2024-08-21T02:06:25.889116Z" }, "jupyter": { "is_executing": true } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "88ab6f7bedd749a8b31016c795e12bf8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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    "labor_cut = mod.simulate(\n",
    "    initial_state=calibrated_values,\n",
    "    final_delta_pct={\"L_s\": 0.5},\n",
    "    optimizer_mode=\"minimize\",\n",
    "    use_euler_approximation=True,\n",
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    "We get back a dictionary with three entries. `euler` and `optimizer` hold `xarray` objects with the results of their respective optimization steps. `optim_res` holds the raw optimizer result returned by `scipy.` We can see from the `optim_res` that optimization was successful. We can also see that during Euler approximation, the errors in the approximation stayed around 0.01, which is good."
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   "execution_count": 23,
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       " message: Optimization terminated successfully.\n",
       " success: True\n",
       "  status: 0\n",
       "     fun: 1.2798018880756698e-17\n",
       "       x: [ 6.156e+03  6.156e+03  3.000e+03  3.500e+03  5.000e-01\n",
       "            8.123e-01  9.196e-09]\n",
       "     nit: 6\n",
       "     jac: [-4.780e-09  1.915e-09  1.009e-09  3.417e-09  2.728e-09\n",
       "           -1.680e-09  3.165e-09]\n",
       "    nfev: 7\n",
       "    njev: 7\n",
       "    nhev: 33"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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    "labor_cut[\"optim_res\"]"
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    "It's also good to check the final value of the `resid` variable, since we know it should always be zero. If it isn't, we have a problem in our model closure. "
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<xarray.DataArray 'resid' (step: 2)> Size: 16B\n",
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  {
   "cell_type": "markdown",
   "id": "448944ba",
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   "source": [
    "### Plotting\n",
    "\n",
    "`cge_model.plotting` offers some plotting functions to examine outputs. The two most important are `plot_bar` and `plot_line`. See the example gallery for how to use these.\n",
    "\n",
    "First, we can look at the total change from the initial to the final state using `gcp.plot_bar`. Since all of the variables have the same dimensions (in this case, no dimensions!) we set `plot_together = True` to see all the plots on a single axis.\n",
    "\n",
    "The default is to plot each variable in its own axis, which makes sense when the variables have different dimensions (for example, one is defined over the set of firms, and other over the set of households).\n",
    "\n",
    "In this case, real demand for capital $K_d$ did not change. This is because it was the *price* of capital $r$ that shifted in response to the reduced labor supply. Capital becomes relatively cheaper as wages go up (remember that implicity $r = \\frac{r^N}{w}$, since wages are the numeraire!), and the net effect is no change to capital demand.\n",
    "\n",
    "On the other hand, the budget line for the household shifts inward (because their endowment of labor has been reduced), which decreases consumpton (and output, which is the same thing in this economy)."
   ]
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cgp.plot_bar(\n",
    "    labor_cut,\n",
    "    mod,\n",
    "    var_names=[\"Y\", \"C\", \"K_d\", \"L_d\", \"r\"],\n",
    "    plot_together=True,\n",
    "    figsize=(14, 4),\n",
    "    legend=True,\n",
    "    metric=\"pct_change\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792bd034",
   "metadata": {},
   "source": [
    "Thanks for the Euler Approximation approach, we can also trace out the effect of each \"step\" in the labor supply reduction on all the other variables. We can see this with `plot_lines`.\n",
    "\n",
    "In this case, we see that the reduction in all variables is essentially linear in the labor supply. This makes sense, since the driving effect is the shifting inward of the household budget constraint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d0aaaf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-21T02:06:28.440888Z",
     "start_time": "2024-08-21T02:06:28.199520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cgp.plot_lines(labor_cut, mod, var_names=[\"Y\", \"C\", \"K_d\", \"L_d\", \"r\"], figsize=(14, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f5481d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.11.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}