{ "cells": [ { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "# Perfect Foresight Model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [ 0 ] }, "outputs": [], "source": [ "# Initial imports and notebook setup, click arrow to show\n", "\n", "from copy import copy\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n", "from HARK.utilities import plot_funcs\n", "\n", "mystr = lambda number: f\"{number:.4f}\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The module `HARK.ConsumptionSaving.ConsIndShockModel` concerns consumption-saving models with idiosyncratic shocks to (non-capital) income. All of the models assume CRRA utility with geometric discounting, no bequest motive, and income shocks that are either fully transitory or fully permanent.\n", "\n", "`ConsIndShockModel` currently includes three models:\n", "1. A very basic \"perfect foresight\" model with no uncertainty.\n", "2. A model with risk over transitory and permanent income shocks.\n", "3. The model described in (2), with an interest rate for debt that differs from the interest rate for savings.\n", "\n", "This notebook provides documentation for the first of these three models.\n", "$\\newcommand{\\CRRA}{\\rho}$\n", "$\\newcommand{\\DiePrb}{\\mathsf{D}}$\n", "$\\newcommand{\\LivPrb}{\\mathsf{S}}$\n", "$\\newcommand{\\PermGroFac}{\\Gamma}$\n", "$\\newcommand{\\Rfree}{\\mathsf{R}}$\n", "$\\newcommand{\\DiscFac}{\\beta}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statement of perfect foresight consumption-saving model\n", "\n", "The `PerfForesightConsumerType` class models the problem of a consumer with Constant Relative Risk Aversion utility specified by\n", "\\begin{align*}\n", "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho},\n", "\\end{align*}\n", "who has perfect foresight about everything except whether he will die between the end of period $t$ and the beginning of period $t+1$, which occurs with probability $\\DiePrb_{t} = 1 - \\LivPrb_t$. Permanent labor income $P_t$ grows from period $t$ to period $t+1$ by factor $\\PermGroFac_{t+1}$.\n", "\n", "At the beginning of period $t$, the consumer has an amount of market resources $M_t$ (which includes both market wealth and current income) and must choose how much of those resources to consume $C_t$, while retaining the rest in a riskless asset $A_t$, which will earn return factor $\\Rfree$. The consumer cannot necessarily borrow arbitrarily; instead, he might be constrained to have a wealth-to-income ratio at least as great as some \"artificial borrowing constraint\" $\\underline{a} \\leq 0$.\n", "\n", "The agent's flow of future utility $U(C_{t+n})$ from consumption is geometrically discounted by factor $\\DiscFac^n$. If the consumer dies, he receives zero utility flow for the rest of time.\n", "\n", "The agent's problem can be written in Bellman form as:\n", "\n", "\\begin{align*}\n", "V_t(M_t,P_t) &= \\max_{C_t}U(C_t) + \\DiscFac \\LivPrb_{t} V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", "& \\text{s.t.} \\\\\n", "A_t &= M_t - C_t, \\\\\n", "A_t/P_t &\\geq \\underline{a}, \\\\\n", "M_{t+1} &= \\Rfree_{t+1} A_t + Y_{t+1}, \\\\\n", "Y_{t+1} &= P_{t+1}, \\\\\n", "P_{t+1} &= \\PermGroFac_{t+1} P_t.\n", "\\end{align*}\n", " \n", "The consumer's problem is characterized by the coefficient of relative risk aversion $\\CRRA$ and intertemporal discount factor $\\DiscFac$, and age-varying sequences of the permanent income growth factor $\\PermGroFac_t$, interest factor $\\Rfree_{t}$ and survival probability $\\LivPrb_t$.\n", "\n", "While it does not reduce the computational complexity of the problem (as permanent income is deterministic, given its initial condition $P_0$), HARK represents this problem with *normalized* variables (represented in lower case), dividing all real variables by permanent income $P_t$ and utility levels by $P_t^{1-\\CRRA}$. The Bellman form of the model thus reduces to:\n", "\n", "\\begin{align*}\n", "v_t(m_t) &= \\max_{c_t}u(c_t) + \\DiscFac \\LivPrb_t \\PermGroFac_{t+1}^{1-\\CRRA} v_{t+1}(m_{t+1}), \\\\\n", "& \\text{s.t.} \\\\\n", "a_t &= m_t - c_t, \\\\\n", "a_t &\\geq \\underline{a}, \\\\\n", "m_{t+1} &= \\Rfree_{t+1} a_t/\\PermGroFac_{t+1} + 1.\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution method for PerfForesightConsumerType\n", "\n", "Because of the assumptions of CRRA utility and no risk other than mortality, the problem has a closed form solution when there is no artificial borrowing constraint. In fact, the consumption function is perfectly linear, and the value function composed with the inverse utility function is also linear. The mathematical solution of this model is described in detail in the lecture notes [PerfForesightCRRA](https://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA).\n", "\n", "The one period problem for this model is solved by the function `solveConsPerfForesight`, which creates an instance of the class `ConsPerfForesightSolver`. To construct an instance of the class `PerfForesightConsumerType`, several parameters must be passed to its constructor as shown in the table below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example parameter values to construct an instance of PerfForesightConsumerType\n", "\n", "| Parameter | Description | Code | Example value | Time-varying? |\n", "| :---: | --- | --- | --- | --- |\n", "| $\\DiscFac$ |Intertemporal discount factor | `DiscFac` | $0.96$ | | \n", "| $\\CRRA$ |Coefficient of relative risk aversion | `CRRA` | $2.0$ | |\n", "| $\\Rfree_t$ | Risk free interest factor | `Rfree` | $[1.03]$ | $\\surd$ |\n", "| $\\LivPrb_t$ |Survival probability | `LivPrb` | $[0.98]$ | $\\surd$ |\n", "|$\\PermGroFac_{t+1}$|Permanent income growth factor|`PermGroFac`| $[1.01]$ | $\\surd$ |\n", "|$\\underline{a}$|Artificial borrowing constraint|`BoroCnstArt`| $None$ | |\n", "|$(none)$|Maximum number of gridpoints in consumption function |`aXtraCount`| $200$ | |\n", "|$T$| Number of periods in this type's \"cycle\" |`T_cycle`| $1$ | |\n", "|(none)| Number of times the \"cycle\" occurs |`cycles`| $0$ | |\n", "\n", "Note that the survival probability and income growth factor have time subscripts; likewise, the example values for these parameters are *lists* rather than simply single floats. This is because those parameters are *time-varying*: their values can depend on which period of the problem the agent is in. All time-varying parameters *must* be specified as lists, even if the same value occurs in each period for this type.\n", "\n", "The artificial borrowing constraint can be any non-positive `float`, or it can be `None` to indicate no artificial borrowing constraint. The maximum number of gridpoints in the consumption function is only relevant if the borrowing constraint is not `None`; without an upper bound on the number of gridpoints, kinks in the consumption function will propagate indefinitely in an infinite horizon model if there is a borrowing constraint, eventually resulting in an overflow error. If there is no artificial borrowing constraint, then the number of gridpoints used to represent the consumption function is always exactly two.\n", "\n", "The last two parameters in the table specify the \"nature of time\" for this type: the number of (non-terminal) periods in this type's \"cycle\", and the number of times that the \"cycle\" occurs. *Every* subclass of `AgentType` uses these two code parameters to define the nature of time. Here, `T_cycle` has the value $1$, indicating that there is exactly one period in the cycle, while `cycles` is $0$, indicating that the cycle is repeated in *infinite* number of times-- it is an infinite horizon model, with the same \"kind\" of period repeated over and over.\n", "\n", "In contrast, we could instead specify a life-cycle model by setting `T_cycle` to $1$, and specifying age-varying sequences of income growth and survival probability. In all cases, the number of elements in each time-varying parameter should exactly equal `T_cycle`.\n", "\n", "The parameter `AgentCount` specifies how many consumers there are of this *type*-- how many individuals have these exact parameter values and are *ex ante* homogeneous. This information is not relevant for solving the model, but is needed in order to simulate a population of agents, introducing *ex post* heterogeneity through idiosyncratic shocks. Of course, simulating a perfect foresight model is quite boring, as there are *no* idiosyncratic shocks other than death!\n", "\n", "The cell below defines a dictionary that can be passed to the constructor method for `PerfForesightConsumerType`, with the values from the table here." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "PerfForesightDict = {\n", " # Parameters actually used in the solution method\n", " \"CRRA\": 2.0, # Coefficient of relative risk aversion\n", " \"Rfree\": [1.03], # Interest factor on assets\n", " \"DiscFac\": 0.96, # Default intertemporal discount factor\n", " \"LivPrb\": [0.98], # Survival probability\n", " \"PermGroFac\": [1.01], # Permanent income growth factor\n", " \"BoroCnstArt\": None, # Artificial borrowing constraint\n", " \"aXtraCount\": 200, # Maximum number of gridpoints in consumption function\n", " # Parameters that characterize the nature of time\n", " \"T_cycle\": 1, # Number of periods in the cycle for this agent type\n", " \"cycles\": 0, # Number of times the cycle occurs (0 --> infinitely repeated)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving and examining the solution of the perfect foresight model\n", "\n", "With the dictionary we have just defined, we can create an instance of `PerfForesightConsumerType` by passing the dictionary to the class (as if the class were a function). This instance can then be solved by invoking its `solve` method." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "PFexample = PerfForesightConsumerType(**PerfForesightDict)\n", "PFexample.cycles = 0\n", "PFexample.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `solve` method fills in the instance's attribute `solution` as a time-varying list of solutions to each period of the consumer's problem. In this case, `solution` will be a list with exactly one instance of the class `ConsumerSolution`, representing the solution to the infinite horizon model we specified." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(PFexample.solution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each element of `solution` has a few attributes. To see all of them, we can use the `vars` built in function:\n", "\n", "the consumption functions reside in the attribute `cFunc` of each element of `ConsumerType.solution`. This method creates a (time varying) attribute `cFunc` that contains a list of consumption functions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': -50.49994992551661, 'hNrm': 50.49994992551661, 'MPCmin': 0.04428139169919579, 'MPCmax': 0.04428139169919579, 'mNrmStE': -50.499910819847685, 'mNrmTrg': nan}\n" ] } ], "source": [ "print(vars(PFexample.solution[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two most important attributes of a single period solution of this model are the (normalized) consumption function `cFunc` and the (normalized) value function `vFunc`. Let's plot those functions near the lower bound of the permissible state space (the attribute `mNrmMin` tells us the lower bound of $m_t$ where the consumption function is defined)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Linear perfect foresight consumption function:\n" ] }, { "data": { "image/png": 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pnzLRsTQ3w4Ruvvh1/DNMdAwx2Zk3bx7+/PNPDB06FJGRkXJbfn4+Fi1ahODgYF3GSEREpPduZ+bj9f+ewjs/n8Xd3CI0q+uMrW8/g4m9/GBjyctWBnkZS0we+Pfff2PcuHEICQmBjY0NiouL4eLiIvvsEBERmUo1Z+PpG5i7LRqZ+cWwsjDD+G5NMO5ZH1hZ6GyhAlJqnp2GDRtix44dSEhIwNmzZ+Ukgu3atYOrK+cLICIi45eUkScnBzwQmyLbQZ4usm+Ov4ez0qFRGTqZqrFBgwbyRkREZCrVnHUnEvHxb+eRVVAMawtzvNuzCf7ZuTEsWc3RO5yXmoiI6Clcv5srqzmHLqXKdssGNbBkWDB86zgpHRo9ApMdIiKiClCrNfjheAIW7jiPnEIVbCzNMamXH8Z0agQLMYkO6S0mO0RERE+QkJaLKRvP4e/4O7Ld1rsmFg0NRuPajkqHRhXAZIeIiOgx1Zzvjl7F4p2xyCtSwc7KAlN7+2FkB2+Ys5pjGsnO3r175e327dtQq9Xl7vvmm28qGxsREZFi4lOyMWVDBE5eu7daQIfGtWQ1p0Ete6VDo+pKdubMmYO5c+eiTZs2qFu3LszEEq5EREQGTqXWYM1f8fhk90UUFKvhYG2B8Oeb4cXQBqzmmFqy89VXX2Ht2rV45ZVXdBsRERGRQi7dysLkDRE4m5gu252buGHBkCDUr8lqjkkmO2L9q44dO+o2GiIiIgUUq9T4+mA8PttzCYUqNZxsLPFhv2b4RxsvXrkwAlrPfPTaa6/hxx9/1G00RERE1exCciYGf3kES3bFykTnOb/a2D2xC4a3bcBEx0hoXdkRi36uWrUKe/bskQt/iqUiylq2bJku4iMiIqoSRSo1vtwfhxX7L6FIpYGzrSVm9Q/AkFaeTHKMjNbJTkREBFq0aCF/joqKKncff0mIiEifRd3IkH1zzidlynbP5u74eFAg6jjbKh0a6VOys3//ft1GQkREVMUKilVYse8yvjwQJ0dd1bS3wuwBARgQUo9f1I0YJxUkIiKTcC4xHZM3nMPFW9my/XyQB+YMCERtJxulQyN9TnbS09OxZs0anD9/XrabN2+OsWPHwsXFRVfxERERVUp+kQrL91zCqoNxUGuAWg7WmDcoEM8H1VU6NKomZhqxTr0WTp48ibCwMNjZ2SE0NFRuO3HiBPLy8rB79260atUKhiAzM1MmZxkZGXB2dlY6HCIi0qFT1+5iyoZziEvJkW1xuUpctnJ1sFY6NKrG87fWyU7nzp3h6+uL1atXw9LyXoGouLhYDkmPj4/HwYMHYQiY7BARGZ+8QhU+2R2LNYevQJzlxKUq0QG5V4CH0qGRISU7oqJz5swZ+Pv7l9seExMjl5DIzc2FIWCyQ0RkXI7Fp2HqxghcTbt3Hhraqj5m9msOF/vyU6SQYXua87fWfXbEjhMSEh5IdhITE+Hk5KTtbomIiLSSU1CMxTsv4Luj12Tbw9lWLvXwnH8dpUMjhWmd7AwfPlx2Rl66dGnpshGHDx/G5MmTMWLECF3GSERE9FhHLqdi6qYIJN7Jk+3/a+uFD/o2g7MtqzlUiWRHJDliToKRI0fKvjriapi1tTXGjRuHhQsX6jZKIiKih8jKL8KC3y/gx2MJsu1Zww4Lhwahc5PaSodGekTrPjslRN+cuLg4+bOPjw/s7Q1rZVj22SEiMkx/XkxB+MYI3MzIl+2X2zfAtD7N4GjDKeRMQWZV9dmZOHEi5s2bBwcHB/nz43BtLCIiqgoZeUX4+LcY/HLyumx7udph0dBgdPRxUzo00lNPleyI0VdFRUWlPz8Kp9wmIqKqsPf8LXywORK3MgsgTjWjOnhjSm8/2FuzmkNVeBnL0PEyFhGR/kvPLcTcbTHYdOaGbDdyc8DiYcFo6+2qdGhkzEPPxbBzLy+vh1ZxxH0NGjTQdtdERESldkUn48MtUUjJKoC5GTC2UyNM7OkHO2sLpUMjA6F1stOoUSMkJSWhTp3y8xekpaXJ+1QqlS7iIyIiE3UnpxCzfo3GtnM3ZduntgOWvBCCVg1qKh0amUqyI65+Payqk52dDVtb28rGRUREJuy3iCTM3BqFtJxCWJib4Z9dGuOd7k1ga8VqDlVDslMyCkskOjNmzCg31FxUc44dO4YWLVpoEQoREZk6calKJDm/RyXLtp+7E5a8EIzg+jWUDo1MKdkpGYUlKjuRkZFyIsES4ueQkBBMmjRJt1ESEZFRE+eUrWdvYva2aKTnFsHS3AxvPeeLt5/zhbWludLhkaklO/v375f/jh49Gp999hlHMBERUaXcyszH9M2R2HP+tmw3r+ssqzkB9VyUDo1Mvc/Ot99+W/pzyeh1zq9DREQVJc4dG05dx7ztMcjML4aVhRkmdGuCN5/1gZUFqzmkO5X6bVqzZg0CAwNlh2RxEz//5z//0V10RERklG6m52H02hOYvCFCJjrB9V2wfXxnjO/ehIkO6U9lZ+bMmXJJiPHjx6NDhw5y29GjR/Hee+/JeXbmzp2ryziJiMhIqjk/n0jEx7+dR3ZBseyP816Ppni9cyNYMskhfZtBuXbt2vj8888xYsSIctt/+uknmQClpqbCEHAGZSKi6pF4JxfhmyLx1+V754eWDWpgybAQ+NZxVDo0MkDVMoOyWCOrTZs2D2xv3bo1iouLtd0tEREZGbVag/8du4aFv19AbqEKNpbmmBzmh9HPNJJz6BBVNa1rhq+88gr+/e9/P7B91apVeOmllyobFxERGYFraTkYsfpvzNwaLROdUG9X7Hy3C17r3JiJDlUby8p2UN69ezfat28v22JCQdFfZ+TIkaWTDwqibw8REZlWNWftkatYsisWeUUq2FlZYGpvP4zs4A1zJjlkKMlOVFQUWrVqJX+Oi4uT/7q5ucmbuK8Eh6MTEZmW+JRsTNkQgZPX7sp2h8a1sGhoMBrU+v9n3CcyiGSnZHJBIiIiQaXWYM1f8fhk90UUFKvhYG2B8Oeb4cXQBqzmkKIqNc4vPz8fx48fx/bt2/Hrr7+W3rZt21apoFauXAlvb285d0+7du3kazzKpk2bZEfpGjVqwMHBQa7L9f3331fq9YmI6OlcupWFIf8+gvk7LshEp3MTN+ye2BUvt2/IRIcMt7Kzc+dO2Uk5LS3tgfvEpSuxKKg21q1bJ/v7fPXVVzLRWb58OcLCwhAbG4s6deo88HhXV1dMnz4d/v7+cm0ukXiJpSzEY8XziIio6hSr1Pj6YDw+23MJhSo1nGwtMaNvc7zQpj67MZDhz7PTpEkT9OrVS04u6O7urrOARILTtm1brFixQrbVajW8vLzk3D3Tpk2r0D5EX6K+ffti3rx5T3ws59khItLO+aRMTN5wDlE3MmW7m38dzB8cBA8XW6VDIxOQWR3z7Ny6dUtWYHSZ6BQWFuLUqVMIDw8v3WZubo4ePXrI2ZmfRORt+/btk1WgRYsWPfQxBQUF8lb2wyIiooorLFbjywOXsXL/ZRSpNHCxs8Ks/s0xuKUnqzmkl7ROdoYNG4YDBw7Ax8dHZ8GIWZfF5a/7EyjRvnDhwiOfJ7I6T09PmcRYWFjgyy+/RM+ePR/62AULFmDOnDk6i5mIyJRE3cjApPXncCE5S7Z7NXfHR4MCUceZ1RwywmRHXGZ64YUXcOjQIQQFBcHKyqrc/RMmTEB1cXJywtmzZ5GdnY29e/fKilPjxo3x7LPPPvBYUTUqOweQqOyIy2RERPRoBcUqfLH3Mv79Z5wcdVXT3gpzBgaif3BdVnPIeJMdsQaWmFBQjJgSFZ6yv+ziZ22SHTFHj6jMiEtkZYm2h4fHI58nLnX5+vrKn8VorPPnz8sKzsOSHRsbG3kjIqKKOZuYjikbzuHirWzZ7htUF3MGBsDNkX9LyciTHTECSlwOEp2GRbKhC2I0lVhbS1RnBg0aVNpBWbTffvvtCu9HPKdsvxwiInp6+UUqfPrHRaw+FA+1BnBztMa8gYHoE1RX6dCIqifZEZ2Jhw8frrNEp4S4xDRq1Cg5d05oaKgcep6TkyOHkwtiKQrRP0dUbgTxr3is6DskEpwdO3bIeXYetm4XERFVzKlrdzB5QwTiU3Jke2CLepjVPwCuDtZKh0ZUfcmOSEjEnDgffPABdEkkUCkpKXJIe3JysrwsJeb0Kem0LNbeKptgiUTorbfewvXr12FnZyfn2/nf//4n90NERE8nr1CFpbtj8c3hKxATk9RxssHHg4PQs7nuRt4SGcw8O6JPzn//+1+EhIQgODj4gQ7KhrL4J+fZISK651h8GqZujMDVtFzZHtqqPmb2aw4X+/J/34lMZp6dyMhItGzZUv5cduFPgT3ziYgMR05BMRbvvIDvjl6T7boutpg/JAjP+T04az2RIeJCoEREJuzw5VRZzbl+N0+2R4R6ycU7nW1ZzSHjoXWyQ0REhisrv0gu2vnT8QTZ9qxhh0VDg9GpiZvSoRHpT7Izd+7cx94vOhgTEZH++fNiCsI3RuBmRr5sv9K+Iab28YejDb//knHS+jd78+bN5dpFRUW4cuUKLC0t5TBwJjtERPolI68IH22PwfpT12W7gau9rOZ08KmldGhE+pnsnDlz5qE9o1999VUMHjy4snEREZEO7T1/Cx9sjsStzAKIMSSvdvTG5DA/2FuzmkPGT+uh548bpdW/f39cvXoVhoBDz4nImKXnFmLOthhsPnNDthu7OWDxsGC08XZVOjQi/R96/ijiRcWNiIiUtTMqGR9uiUJqdgHMzYDXOjfGxJ5NYWtloXRoRNVK62Tn888/L9cWBaKkpCS5VEOfPn10ERsREWkhLbsAM3+Nxm8RSbLtW8cRS4YFo2WDmkqHRmRYyc6nn35ari2WcKhdu7ZcRiI8PFwXsRER0VMQXzp/i0zCzK3RuJNTCAtzM7zRpTEmdG/Cag6ZNK2THTHyioiI9ENKVgFmbInCzuhk2fb3cMKSYSEIqu+idGhEhpvs5OXlyW8R9vb2sn3t2jU5HL158+bo1auXLmMkIqJHEH+Ht569idnbopGeWwRLczO89Zwv3n7OF9aW//+iyUSmTOtkZ+DAgRgyZAjefPNNpKenIzQ0FNbW1khNTZWLgI4bN063kRIRUTm3MvMxfXMk9py/LdsB9ZzlSKuAeqzmEJWlddp/+vRpdO7cWf68YcMGeHh4yOqOWAn9/s7LRESk22rOLycT0WPZnzLRsbIww6ReTbHlX88w0SHSZWUnNzcXTk5O8ufdu3fLKo/opNy+fXuZ9BARke7dTM9D+KZIueSDEFLfBUteCEFT93t/j4lIh5UdX19fbNmyBYmJidi1a1dpP53bt29zcj4ioiqo5vx4LAG9Pj0oEx3RH2daH39sHNeRiQ5RVVV2xNpXL774It577z10794dHTp0KK3ytGzZUtvdEhHRfRLv5GLapggcvpwm260a1MDiYSFy/hwiquLlIpKTk+VEgiEhIfISlnD8+HFZ2fH394ch4HIRRKSv1GoN/nfsGhb+fgG5hSrYWpljUi8/jH6mkZxDh8iUZVbXchGiU7K4lSVGZRERUeVcTc3B1I0ROHbljmyHNnLF4qHB8HZzUDo0IoNTqWRn79698ib66ajV6nL3ffPNN5WNjYjI5KjUGqw9chVLdl1AfpEa9tYWmNrbH6+0bwhzVnOIqjfZmTNnDubOnYs2bdqgbt26MDPjf0IiosqIS8nGlA0ROHXtrmx39KmFRUOD4eV6b/JWIqrmZOerr77C2rVr8corr2i7CyIi+n/VnP8ciscnf1xEYbEajjaWCH/eHy+GNuAXSSIlk53CwkJ07NhRFzEQEZmsS7eyMGlDBM4lpst25yZuWDg0GJ417JQOjchoaD3PzmuvvYYff/xRt9EQEZmIIpUaK/dfRt/P/5KJjpOtpeyA/N8xoUx0iPSlspOfn49Vq1Zhz549CA4OhpWVVbn7xfpYRET0oPNJmZi84RyibmTKdjf/Opg/OAgeLrZKh0ZklLROdiIiItCiRQv5c1RUVLn7eI2ZiOhBoj+OqOaIW7FaAxc7K8we0ByDWnjy7yaRPiY7+/fv120kRERGLOpGBiatP4cLyVmyHRbgjnmDAlHHidUcIr2eZ4eIiB6voFiFz/dewld/xstRV64O1pgzIAD9gjllB5FBJDvp6elYs2YNzp8/L9vNmzfH2LFj5fTNRESm7mxiOiavP4dLt7Nlu29wXcwdEIBajjZKh0ZkUrReG+vkyZMICwuDnZ1d6RIRJ06cQF5enlwMtFWrVjAEXBuLiHQtv0iFT/+4iNWH4qHWAG6O1vhoUCB6B9ZVOjQio/E052+tk53OnTvD19cXq1evhqXlvQJRcXGxHJIeHx+PgwcPwhAw2SEiXTp17Q4mr49AfGqObA9qUQ+z+gegpoO10qERGZVqSXZERefMmTMPrG4eExMjl5DIzc2FIWCyQ0S6kFtYjKW7LuLbI1cg/qrWcbKRw8l7NHdXOjQio1Qtq56LHSckJDyQ7CQmJsLJyUnb3RIRGZy/49PkCuXX0u59yRvWuj5m9G0OF/vy848RkTK0TnaGDx8uOyMvXbq0dNmIw4cPY/LkyRgxYoQuYyQi0ks5BcVYtPMC/nv0mmzXdbHF/CFBeM6vjtKhEZEukh2R5IhhkyNHjpR9dQQxi/K4ceOwcOFCbXdLRGQQDl9OldWc63fzZHtEqBfCn28GZ1tWc4j0jdZ9dkqIvjlxcXHyZx8fH9jb28OQsM8OET2NrPwizN9xAT8dT5BtsY7VoqHB6NTETenQiExKZnX02VmwYAHc3d0xZswYBAUFlW7/5ptvkJKSgqlTp2q7ayIivXQg9jbCN0UiKSNftkd2aIgpvf3haMP5WYmMctXzr7/++oHOyUJAQAC++uqrysZFRKQ3MnKL5FIPr357QiY6DWvZ4+d/tsfcgYFMdIgMgNb/S5OTk1G37oMTZNWuXRtJSUmVjYuISC/sibmFDzZH4nZWAcTqDqM7NsKksKawt2aSQ2QotP7f6uXlJUdfNWrUqNx2sa1evXq6iI2ISDF3cwoxZ1s0tpy9KduN3RyweFgw2ni7Kh0aEVVXsvP666/j3XffRVFREbp16ya37d27F1OmTMH777+v7W6JiBS3MyoJH26JRmp2AczNgNc7N8Z7PZvC1spC6dCIqDqTHTGfTlpaGt566y0UFhbKbba2trJjcnh4uLa7JSJSTFp2AWb+Go3fIu5dim9Sx1FWc1o2qKl0aESk5NDz7Oxsueq5WD6iSZMmsLExrNV8OfSciMSfwe0RSZj1azTu5BTCwtwMb3ZtjAndm8DGktUcIpMdel7C0dERbdu2rexuiIgUcTsrHzO2RGFX9C3Z9vdwwpJhIQiq76J0aESkIxxOQEQmW83ZfOYG5myLQUZeESzNzfCv53zlzdpS61k5iEgPMdkhIpOTnJGP6ZsjsffCbdkOqOcsqznN6/FSNpExYrJDRCZVzVl/6jrmbY9BVn4xrC3MMaG7L97o6gMrC1ZziIwVkx0iMgk30vPkUg8HL6bIdkh9Fyx5IQRN3Z2UDo2I9CnZmThxYoUfu2zZMm3iISLSeTXnx+MJWLDjArILimV/nPd7NsXYTo1gyWoOkUl4qmTnzJkz5dqnT59GcXEx/Pz8ZPvixYuwsLBA69atdRslEZEWEu/kYurGCByJS5Pt1g1rynlzfGo7Kh0aEelrsrN///5ylRsnJyd89913qFnz3oRbd+/exejRo9G5c2fdR0pEVEFqtQbf/30Ni3ZeQG6hCrZW5pgc5o9XO3rLOXSIyLRoPamgp6cndu/eLVc5LysqKgq9evXCzZv31pPRd5xUkMi4XE3NwZSNETh+5Y5shzZyxeKhwfB2c1A6NCIytEkFxYukpNzr6FeW2JaVlaXtbomItKJSa/Dt4StYujsW+UVq2FtbYFoff7zcriHMWc0hMmlaJzuDBw+Wl6w++eQThIaGym3Hjh2Ta2YNGTJElzESET3W5dvZmLLhHE4npMv2M761sHBIMLxc7ZUOjYgMOdn56quvMGnSJLz44oty5XO5M0tLjB07FkuWLNFljERED1WsUuM/f13Bsj8uorBYDUcbS3zwfDOMCPWCmRmrOUSko4VAc3JyEBcXJ3/28fGBg4NhXRdnnx0iwxSbnCWrOeeuZ8h216a1sWBIEOrVsFM6NCIytoVARXITHBxc2d0QEVVIkUqNrw7E4fN9l1Ck0sDJ1hIz+zXHsNb1Wc0hooeq1Ixahw4dwssvv4wOHTrgxo0bctv333+Pv/76qzK7JSJ6qJibmRi08jA++eOiTHS6+9fBH+91xQtteNmKiKog2dm4cSPCwsJgZ2cnJxssKCiQ20U5af78+drulojoAaI/zqd/XMSAFX8h+mYmXOys8OnwEPxnVBt4uNgqHR4RGWuy89FHH8lOyqtXr4aVlVXp9meeeUbOrExEpAuR1zNkkvPZ3ksoVmsQFuCOPyZ2weCWvGxFRBWjdZ+d2NhYdOnS5YHtorNQevq94Z9ERNoqKFbhsz2X8PXBeDmHjquDNeYODEDfoLpMcoioepIdDw8PXL58Gd7e3uW2i/46jRs31na3REQ4k3AXkzdEyPlzhH7BdTFnQABqOdooHRoRmVKy8/rrr+Odd97BN998I79lieUhjh49KufemTFjhm6jJCKTkF+kknPm/OdQPNQawM3RBh8NCkTvQA+lQyMiU+yzM23aNDmhYPfu3ZGdnS0vab322mt44403MH78+EoFtXLlSlkxsrW1Rbt27XD8+PFHPlb0GRILj4rFSMWtR48ej308Eemnk1fv4PnPDmHVwXuJzuCWnvjjvS5MdIhI+UkFCwsL5eUskfA0b94cjo6OlQpo3bp1GDlypOz8LBKd5cuXY/369bKPUJ06dR54/EsvvSQ7RXfs2FEmR4sWLcLmzZsRHR0tFyt9Ek4qSKSs3MJiLNkVi7VHrkL8NXJ3tsH8wUHo3sxd6dCISI89zflb62SnW7du6Nq1K2bNmlVu+927dzF06FDs27dPm93KBKdt27ZYsWKFbKvVanh5eclqkagmPYlKpZIVHvF8kTTdTwyRLxkmX/Jhif0z2SGqfkfj0jB1YwQS7uTK9gut6+PDfs3l0HIiIl0lO1pfxjpw4IBMKAYNGiSXjChb6fnzzz+12qd47qlTp+SlqNIAzc1lW/QHqojc3Fy5Vperq+tD71+wYIH8cEpuItEhouqVXVCMGVuiMGL13zLRqedii7Wj22LJCyFMdIhIv2ZQ3rNnD5KTk9G+fXtcvXq10sGkpqbKyoy7e/nytWiL16mIqVOnol69euUSprLCw8NlFlhyS0xMrHTcRFRxf11KRdinB/H939dk+8V2DbDrvS541u/By9RERLpQqbWx6tatK6s4o0ePlpeeRN+aZs2aQSkLFy7Ezz//LKtOov/Ow9jY2MgbEVWvzPwizP/tPH4+ce8LRv2adlg0NBjP+LopHRoRGTmtk52SSb1E4vDjjz/KGZV79+4tKyvacnNzg4WFBW7dulVuu2iLeX0eZ+nSpTLZEdUmLkxKpF/2x97GB5sikZSRL9sjOzTE1N7+cLCp9FrERERPpPVfmvv7NX/44YeyqjNq1Chtdwlra2u0bt0ae/fulX2BSjooi/bbb7/9yOctXrwYH3/8MXbt2oU2bdpo/fpEpFsZuUWY91sMNpy6LtsNa9nLak77xrWUDo2ITIjWyc6VK1dkJaYsMQrLz89PdjLW1sSJE2XCJJKW0NBQOfRcdIAWl8oEMcJKDCkXHY0FMdR85syZsrok5uYp6dsjhsBXdhg8EWlvT8wtfLA5ErezCiAKwWOeaYRJvfxgZ22hdGhEZGK0TnYaNmz40O2BgYHypq3hw4cjJSVFJjAicWnRogV27txZ2mk5ISFBjtAq8e9//1uO4ho2bFi5/Ygh8bNnz9Y6DiLSzt2cQszeFo2tZ2/KduPaDlgyLBitGz58hCQRUVV7qnl2RNVl3rx5cHBwkD8/zrJly2AIOKkgke78HpmEGVujkJpdCHMz4PUujfFej6awtWI1h4iUO38/VWXnzJkzcg6bkp8fhSsSE5mW1OwCzNoajd8ik2S7SR1HOWdOC68aSodGRFT55SIMHSs7RNoTfz62RSRh9q/RuJNTCAtzM4zr6oPx3X1hY8lqDhEZYGWHiKjE7ax8fLg5Crtj7k0V4e/hhKUvhCDQ00Xp0IiItE92ntRPxxD77BDR01dzNp+5gTnbYpCRVwRLczO83c0Xbz3rC2vLSk3KTkRUJZ66z05FsM8OkXFKzsiXw8n3Xbgt24GezlgyLATN6vISMBEZSbKzf//+qouEiPS6mvPLyUR8tP08sgqKYW1hjnd6NMEbXRrD0oLVHCLSb5XusxMTEyPnvhFz3ZSt7PTv37+yuyYiPXAjPQ/TNkbg0KVU2Q7xqoGlw4LRxN1J6dCIiKo22YmPj8fgwYMRGRkpk5uSQV0ll7DE6uVEZLjE/+kfjyfIxTtzClWyP877PZtibKdGrOYQkUHR+i/WO++8g0aNGuH27duwt7dHdHQ0Dh48KJd5EKuOE5HhSryTi5f+cwzTN0fJRKd1w5r4/Z3OeKOrDxMdIjKdys7Ro0exb98+uT6WWL5B3Dp16iTXrJowYUKFOzMTkf5QqzX4/u9rWLTzAnILVbC1MseUMH+M6ugt59AhIjKpZEdcpnJyunfNXiQ8N2/elIuAijWzYmNjdRkjEVWDK6k5mLohAsev3pHtdo1csXhYMBrWclA6NCIiZZIdsdjnuXPn5KWsdu3aYfHixbC2tsaqVavQuHHjykVFRNVGpdbg28NXsHR3LPKL1LC3tkB4H3+81K4hzFnNISJTTnY+/PBD5OTkyJ/nzp2Lfv36oXPnzqhVqxbWrVunyxiJqIpcvp2NKRvO4XRCumx38nXDgiFB8HK1Vzo0IiL9XBvrzp07qFmzpkFNKsi1scgUFavUWH3oCj7dcxGFxWo42lhiet9m+L+2Xgb1/5eITFemUmtjubq66nJ3RFQFYpOzZDXn3PUM2e7atLas5tSrYad0aEREVaJSyU5+fj4iIiLk8HO1Wl3uvgEDBlQ2NiLSoSKVGl8diMPn+y6hSKWBs60lZvRrjmGt67OaQ0RGTetkZ+fOnRg5ciRSU+/NqlqW+MPJSQWJ9Ef0zQxMXh+BmKRM2e7RrA4+HhwEd2dbpUMjIqpyWs8ONn78eLzwwgtISkqSVZ2yNyY6RPpB9MdZtjsWA1cclolODXsrfPZ/LbB6ZBsmOkRkMrSu7Ny6dQsTJ06Eu7u7biMiIp2IuJ4uqzmxt7Jku3eAB+YNCkRtJxulQyMiMoxkZ9iwYXJZCB8fH91GRESVkl+kwud7L+Hrg/FyDh1XB2vMHRiAvkF12TeHiEyS1kPPc3Nz5WWs2rVrIygoCFZWVuXuF0tGGAIOPSdjcibhLiZviJDz5wj9gutizoAA1HJkNYeIjEu1DD3/6aefsHv3btja2soKT9lvjOJnQ0l2iIylmvPJ7lis+esK1BrAzdEGHw0KRO9AD6VDIyJSnNbJzvTp0zFnzhxMmzZNLgJKRMo4cfUOpmyIkGtbCUNaemJm/+aoYW+tdGhERIad7BQWFmL48OFMdIgUkltYjMU7Y/Hd0asQF6PdnW0wf3AQujfjoAEiorK0zlRGjRrFNbCIFHI0Lg29lx/C2iP3Ep1/tKmP3e91ZaJDRKTLyo6YS0esdL5r1y4EBwc/0EF52bJl2u6aiB4hu6AYC38/j//9nSDb9VxssWBosFzygYiIdJzsREZGomXLlvLnqKiocvdxeCuR7h26lIJpGyNxIz1Ptl9s1wDhffzhZFv+iwYREeko2dm/f7+2TyWip5CZX4T5v53HzycSZbt+TTssHhqMjr5uSodGRGS8fXaKiorQvXt3XLp0SfcREVGp/Rduo9eyg6WJzqsdvbHr3S5MdIiIqrqyI/rniNXOiahqZOQWYc72aGw6fUO2vWvZY/GwEIQ2clU6NCIi0xmN9fLLL2PNmjW6jYaIsDs6GT0+/VMmOqL722udGuH3d7ow0SEiqu4+O8XFxfjmm2+wZ88etG7dGg4ODuXu52gsoqdzJ6cQs3+Nxq/nbsp249oOWDIsGK0bMskhIlIk2REjsFq1aiV/vnjxYrn7OBqL6On8HpmEGVujkJpdCHMz4PUujfFej6awtbJQOjQiIoPH0VhECkrNLsDMrVHYEZks203dHbFkWAhCvGooHRoRkdHQOtkR0tPTZb+d8+fPy3ZAQADGjBkjVyElokfTaDTycpW4bHU3twgW5mZ461kfvN3NFzaWrOYQEemSmUb81dXCyZMnERYWBjs7O4SGhsptJ06cQF5enlwNveQSlzEtEU+kC7cz8zF9SxT+iLkl283qOsu+OYGe/JJARFQV52+tk53OnTvD19cXq1evhqWlZWmn5ddeew3x8fE4ePAgDAGTHaou4r+aGGE1d3sMMvKKYGVhhrefa4Jxz/rA2pIL6hIR6V2yIyo6Z86cgb+/f7ntMTExaNOmDXJzc2EImOxQdUjKyMMHmyKxPzZFtoM8XbDkhWD4e/B3joioqs/fWvfZETtOSEh4INlJTEyEk5OTtrslMiriu8QvJxPx0fbzyCoohrWFOd7p0QRvdGkMSwtWc4iIqoPWyc7w4cMxduxYLF26FB07dpTbDh8+jMmTJ2PEiBG6jJHIIF2/m4vwTZE4dClVtlt41ZB9c5q488sAEZFBJDsiyRHz6YwcOVL21SlZRmLcuHFYuHChLmMkMihqtQY/HE/Awh3nkVOogo2lOSb18sOYTo3kqCsiIqpeWvfZKSH65ly+fFkmPj4+PrC3t4chYZ8d0qWEtFxM3RiBo/Fpst3WuyYWDQ1G49qOSodGRGRUqqXPTgmR3AQHB1d2N0QGX8357uhVLN4Zi7wiFWytzDG1tz9GdfCGOas5RESKeupkx9zc/InLQYj7Sy5tERm7K6k5mLLhHE5cvSvb7Rq5YvGwYDSsVX69OCIiMpBkZ/PmzY+87+jRo/j888+hVqsrGxeR3lOpNfj28BUs2RWLgmI17K0tEN7HHy+1a8hqDhGRISc7AwcOfGBbbGwspk2bhm3btuGll17C3LlzdRUfkV66fDsLkzdE4ExCumx38nXDgiFB8HI1rD5rRESmoFJ9dm7evIlZs2bhu+++k0tHnD17FoGBgbqLjkjPFKvUWHUoHsv3XEJhsRpONpaY3rcZhrf1euLlXSIiMqBkR/R8nj9/Pr744gu0aNECe/fulctHEBmz2GRRzTmHiOsZsv2sX23MHxyEejXslA6NiIh0mewsXrwYixYtgoeHB3766aeHXtYiMiZFKjX+fSAOX+y7hCKVBs62lpjZPwBDW3mymkNEZIzz7IjRWGJdrB49esDCwuKRj9u0aRMMAefZoceJvpmByesjEJOUKds9mrnj48GBcHe2VTo0IiKTllmV8+yIGZP5bZaMneiPs2LfJXx5IA7Fag1q2FthzoAADAipx99/IiID89TJztq1a6smEiI9EXE9XVZzYm9lyXafQA/MHRiI2k42SodGRERaqPQMykTGIr9IJUdZrToYB7UGqOVgjXmDAvF8UF2lQyMiokpgskME4NS1u3IW5LiUHNkWl6tmDwiAq4O10qEREVElMdkhmHo155PdsfjPX1cguuq7OdrIDshhAR5Kh0ZERDrCZIdM1omrdzBlQ4Rc20oY0tITM/s3Rw17VnOIiIwJkx0yObmFxXJ1crFKuajmuDvbyKUeuvm7Kx0aERFVASY7ZFKOxKVi6sYIJN7Jk+3hbbzwQd9mcLGzUjo0IiKqIkx2yCRkFxRjwY7z+OFYgmx71rCT1ZwuTWsrHRoREVUxJjtk9A5eTEH4pkjcSL9XzXmpXQNM6+MPJ1tWc4iITAGTHTJamflF+Hj7eaw7mSjbXq52WDQkGB193ZQOjYiIqhGTHTJK+y/cltWc5Mx82X61ozcmh/nBwYa/8kREpoZ/+cmopOcWYu72GGw6fUO2vWvZY/GwEIQ2clU6NCIiUog59MzKlSvh7e0NW1tbtGvXDsePH3/kY6OjozF06FD5eLE44/Lly6s1VtIvu6OT0fPTgzLREWt1vt65EX5/pwsTHSIiE6dXyc66deswceJEzJo1C6dPn0ZISAjCwsJw+/bthz4+NzcXjRs3xsKFC+HhwRlvTdWdnEKM/+kM/vn9KaRkFcCntgM2juuI6X2bw87aQunwiIhIYWYajZhWTT+ISk7btm2xYsUK2Var1fDy8sL48eMxbdq0xz5XVHfeffddeXucgoICeSuRmZkpXyMjIwPOzs46eidUXX6LSMLMrVFIyymEuRnwRlcfvNO9CWytmOQQERmzzMxMuLi4VOj8rTeVncLCQpw6dQo9evQo3WZubi7bR48e1dnrLFiwQH44JTeR6JDhSc0uwFs/nMK/fjwtEx0/dyds+dczmNrbn4kOERHpZ7KTmpoKlUoFd/fyU/aLdnJyss5eJzw8XGaBJbfExHvDkskwiELk1rM30HPZn9gRmQxLczNM6OaLX8c/g+D6NZQOj4iI9JDJjcaysbGRNzI8tzPzMX1LFP6IuSXbzeo6Y8mwYAR6uigdGhER6TG9SXbc3NxgYWGBW7funchKiDY7H5s2Uc3ZePoG5m6LRmZ+MawszDC+WxOMe9YHVhZ6U5wkIiI9pTdnCmtra7Ru3Rp79+4t3SY6KIt2hw4dFI2NlJOUkYcxa09g0vpzMtEJ8nTBtvGdMKF7EyY6RERkWJUdQQw7HzVqFNq0aYPQ0FA5b05OTg5Gjx4t7x85ciQ8PT1lJ+OSTs0xMTGlP9+4cQNnz56Fo6MjfH19FX0vVPlqzroTifj4t/PIKiiGtYU53u3ZBP/s3BiWTHKIiMhQk53hw4cjJSUFM2fOlJ2SW7RogZ07d5Z2Wk5ISJAjtErcvHkTLVu2LG0vXbpU3rp27YoDBw4o8h6o8q7fzZVLPRy6lCrbLRvUkH1zfOs4KR0aEREZIL2aZ0ffx+lT1VKrNfjheAIW7jiPnEIVbCzNMamXH8Z0agQLMYkOERGRFudvvarskOlKSMvFlI3n8Hf8Hdlu610Ti4YGo3FtR6VDIyIiA8dkhxSv5nx39CoW74xFXpEKdlYWmNrbDyM7eMOc1RwiItIBJjukmPiUbEzZEIGT1+7KdofGtWQ1p0Ete6VDIyIiI8Jkh6qdSq3Bmr/i8cnuiygoVsPB2gLhzzfDi6ENWM0hIiKdY7JD1ery7SxMWh+Bs4npst25iRsWDAlC/Zqs5hARUdVgskPVolilxqpD8Vi+5xIKi9VwsrHEh/2a4R9tvGBmxmoOERFVHSY7VOUuJGdi8voIRN7IkO3n/Gpj/pAg1HWxUzo0IiIyAUx2qMoUqdT4cn8cVuy/hCKVBs62lpjVPwBDWnmymkNERNWGyQ5ViagbGZi8IQLnkzJlu2dzd3w8KBB1nG2VDo2IiEwMkx3SqYJiFVbsu4x/H4hDsVqDmvZWmD0gAANC6rGaQ0REimCyQzpzLjEdkzecw8Vb2bL9fJAH5gwIRG0nG6VDIyIiE8Zkhyotv0glR1mtOhgHtQao5WCNeYMC8XxQXaVDIyIiYrJDlXPq2l1M2XAOcSk5si0uV4nLVq4O1kqHRkREJDHZIa3kFarwye5YrDl8BRoN5KUq0QG5V4CH0qERERGVw2SHntqx+DRM3RiBq2m5sj20VX3M7NccLvZWSodGRET0ACY7VGE5BcVYvPMCvjt6TbY9nG3lUg/P+ddROjQiIqJHYrJDFXLkciqmbopA4p082f6/tl74oG8zONuymkNERPqNyQ49VnZBMRbsOI8fjiXItmcNOywcGoTOTWorHRoREVGFMNmhRzp4MQXhmyJxI/1eNefl9g0wrU8zONrw14aIiAwHz1r0gIy8Inz8Wwx+OXldtr1c7bBoaDA6+rgpHRoREdFTY7JD5ey7cAsfbIpCcmY+xOoOozp4Y0pvP9hb81eFiIgME89gJKXnFmLuthhsOnNDthu5OWDxsGC09XZVOjQiIqJKYbJD2BWdjA+3RCElqwDmZsDYTo0wsacf7KwtlA6NiIio0pjsmLA7OYWY9Ws0tp27Kds+tR2w5IUQtGpQU+nQiIiIdIbJjon6LSIJM7dGIS2nEBbmZvhnl8Z4p3sT2FqxmkNERMaFyY6JEZeqRJLze1SybPu5O2HJC8EIrl9D6dCIiIiqBJMdE6HRaLD17E3M3haN9NwiWJqb4a3nfPH2c76wtjRXOjwiIqIqw2THBNzKzMf0zZHYc/62bDev6yyrOQH1XJQOjYiIqMox2THyas7G0zcwd1s0MvOLYWVhhgndmuDNZ31gZcFqDhERmQYmO0YqKSNPLvVwIDZFtoPru2DJsBD4eTgpHRoREVG1YrJjhNWcn08kYv5v55FVUCz747zXoyle79wIlqzmEBGRCWKyY0QS7+TKas5fl1Nlu2WDGlgyLBi+dVjNISIi08Vkxwio1Rr8cOwaFv5+ATmFKthYmmNymB9GP9NIzqFDRERkypjsGLhraTmYujECf8ffke1Qb1csGhYs17YiIiIiJjsGXc1Ze+QqluyKRV6RCnZWFpja2w8jO3jDnNUcIiKiUkx2DFB8SjambIjAyWt3ZbtD41pYNDQYDWrZKx0aERGR3mGyY0BUag3W/BWPT3ZfREGxGg7WFgh/vhleDG3Aag4REdEjMNkxEJduZWHShgicS0yX7c5N3LBwaDA8a9gpHRoREZFeY7Kj54pVanx9MB6f7bmEQpUaTraWmNG3OV5oUx9mZqzmEBERPQmTHT12PikTkzecQ9SNTNnu5l8H8wcHwcPFVunQiIiIDAaTHT1UWKzGlwcuY+X+yyhSaeBiZ4VZ/ZtjcEtPVnOIiIieEpMdPRN1IwOTN0TIqo7Qq7k7PhoUiDrOrOYQERFpg8mOnigoVuGLvZfx7z/j5KirmvZWmDMwEP2D67KaQ0REVAlMdvSAGGEl+uZcvJUt232D6mLOwAC4OdooHRoREZHBY7KjoPwiFT7dcxGrD8ZDrQHcHK0xb2Ag+gTVVTo0IiIio8FkRyGnrt2RfXPiU3Jke2CLepjVPwCuDtZKh0ZERGRUmOxUs7xCFZbujsU3h69AowHqONng48FB6NncXenQiIiIjBKTnWp0LD5NrlB+NS1Xtoe2qo+Z/ZrDxd5K6dCIiIiMFpOdapBTUIzFOy/gu6PXZLuuiy3mDwnCc351lA6NiIjI6DHZqWKHL6fKas71u3myPSLUSy7e6WzLag4REVF1YLJTRbLyizB/xwX8dDxBtsWCnYuGBqNTEzelQyMiIjIpTHaqwJ8XUxC+MQI3M/Jl+5X2DTG1jz8cbfhxExERVTeefXUoI68IH22PwfpT12W7gau9rOZ08KmldGhEREQmi8mOjuw9fwsfbI7ErcwCiNUdXu3ojclhfrC35kdMRESkJJ6JKyk9txBztsVg85kbst3IzQGLhwWjrber0qERERERk53K2RmVjA+3RCE1uwDmZsBrnRtjYs+msLWyUDo0IiIi+n+Y7GghLbsAs36NxvaIJNn2reOIJcOC0bJBTaVDIyIiovsw2XkKGo0Gv0UmYdbWaKTlFMLC3AxvdGmMCd2bsJpDRESkp5jsVFBKVgFmbInCzuhk2fb3cMKSYSEIqu+idGhERET0GEx2KlDN2Xr2JmZvi0Z6bhEszc3w1nO+ePs5X1hbmisdHhERET0Bk53HuJWZj+mbI7Hn/G3ZDqjnLEdaBdRjNYeIiMhQMNl5RDVHTAw4b3sMsvKLYWVhhne6N8EbXX1gZcFqDhERkSFhsnOfm+l5CN8UKZd8EELqu2DJCyFo6u6kdGhERESkBb0sU6xcuRLe3t6wtbVFu3btcPz48cc+fv369fD395ePDwoKwo4dO7Sq5vx4LAG9Pj0oEx3RH2daH39sHNeRiQ4REZEB07tkZ926dZg4cSJmzZqF06dPIyQkBGFhYbh9+16/mfsdOXIEI0aMwNixY3HmzBkMGjRI3qKiop7qdV//70m53EN2QTFaNaiBHRM6482uPrDkZSsiIiKDZqYRJQ09Iio5bdu2xYoVK2RbrVbDy8sL48ePx7Rp0x54/PDhw5GTk4Pt27eXbmvfvj1atGiBr7766oHHFxQUyFuJjIwMNGjQAJ7j1sLO0RETuvni5fbecg4dIiIi0k+ZmZkyP0hPT4eLyxMGDmn0SEFBgcbCwkKzefPmcttHjhypGTBgwEOf4+Xlpfn000/LbZs5c6YmODj4oY+fNWuWSO5444033njjjTcY/i0xMfGJ+YVedVBOTU2FSqWCu7t7ue2ifeHChYc+Jzk5+aGPF9sfJjw8XF4mKyEqR3fu3EGtWrVgJpYrr8LsMzExEc7OzlXyGlQxPBb6g8dCv/B46A8ei4oRF6aysrJQr169Jz5Wr5Kd6mBjYyNvZdWoUaNaXlv80vIXVz/wWOgPHgv9wuOhP3gsnuyJl6/+H73qfevm5gYLCwvcunWr3HbR9vDweOhzxPaneTwRERGZFr1KdqytrdG6dWvs3bu33GUm0e7QocNDnyO2l3288Mcffzzy8URERGRa9O4yluhPM2rUKLRp0wahoaFYvny5HG01evRoef/IkSPh6emJBQsWyPY777yDrl274pNPPkHfvn3x888/4+TJk1i1ahX0hbhsJobS33/5jKofj4X+4LHQLzwe+oPHwgSGngti2PmSJUtkJ2MxhPzzzz+XQ9KFZ599Vk44uHbt2nKTCn744Ye4evUqmjRpgsWLF+P5559X8B0QERGRvtDLZIeIiIjIKPvsEBEREekakx0iIiIyakx2iIiIyKgx2SEiIiKjxmSniomRY2IZirK3hQsXlntMREQEOnfuDFtbWzlFuBhNRlVHLAQrRvmJY3H27Nly9+3atUsuJOvk5ITatWtj6NChcpQfVf+xEGMnli5diqZNm8ohuGLKiY8//lixWE39eJS4fPmy/P9RXTPPm6pHHYsDBw5g4MCBqFu3LhwcHORjfvjhB0VjNQRMdqrB3LlzkZSUVHoTK7iXXQOlV69eaNiwIU6dOiWH3M+ePVuv5gkyNlOmTHnoWipXrlyRf0S6desm/7iIxEes1zZkyBBF4jTlY1Eyh9Z//vMfmfCItfF+/fVXOfcWKXM8hKKiIowYMUJ+OSNljsWRI0cQHByMjRs3yi/KYg46Mf/c9u3bFYnTYDzFouSkhYYNGz6wKntZX375paZmzZpyxfcSU6dO1fj5+VVThKZlx44dGn9/f010dLRcLffMmTOl961fv15jaWmpUalUpdt+/fVXjZmZmaawsFChiE3zWMTExMhjceHCBUVjNCWPOx4lpkyZonn55Zc13377rcbFxUWROE1BRY5FWc8//7xm9OjR1RafIWJlpxqIy1ZiVfWWLVvKyk1xcXHpfUePHkWXLl3kUhklwsLCEBsbi7t37yoUsXESa6a9/vrr+P7772Fvb//A/WKpEnNzc3z77bdQqVTIyMiQj+3RowesrKwUidlUj8W2bdvQuHFj+W21UaNG8nLwa6+9hjt37igSr6kfD2Hfvn1yAteVK1dWe3ympCLH4n7ib5Wrq2uVx2bImOxUsQkTJsglLPbv34833ngD8+fPl+XJEmKWaHd393LPKWmL+0g3RP+PV199FW+++aZciuRhxEl19+7d+OCDD2QfEdEn4fr16/jll1+qPV5TPxbx8fG4du2aPLn+97//lTOmi8u8w4YNq/Z4jV1FjkdaWpp8jDgOXIVb2WNxP/H36cSJE6VLKtHDMdnRwrRp0x7odHz/TfQxKFnrSyxxIa6xil9gsYbXF198ITufUfUdC/GZZ2VlITw8/JH7Esml+EYl1mYTfzz+/PNPWXETJ1hONF69x0IsACz+j4hER/QPEf+H1qxZI780iKonVe/xEP8vXnzxRVmFJmWPRVni/4NIclavXo2AgIAqfx+GjMtFaCElJUV+03kcUYIve2mqRHR0NAIDA+Uvtp+fn+xYJjopb9mypdwvsOgkK0r2NWvWrJL3YGrH4h//+Ie8NCL+qJQQl6osLCzw0ksv4bvvvsOMGTOwc+dOmeiUEJUdMUJOXG4Uo7Soeo6FWARRVEFFh9gSeXl5sqwvqm89e/as0vdiDHR5PESVMzs7u/R+cdoQCal4jBhMMWbMmCp9L4ZOl8eihPgyJha/XrZsGf75z39WafzGQO9WPTcEYkiyuGlDjPIR/ULq1Kkj2x06dMD06dPlH/WSfiF//PGHTISY6OjuWIjFZD/66KPS9s2bN2XfqHXr1pUuMpubmyuPTVnij4wg/rBT9R2LZ555RvZti4uLg4+Pj9x28eJF+a8YuUjVezxEsi9OuiW2bt2KRYsWyZFBYkoAqr5jUTL8vF+/fvIYMNGpIKV7SBuzI0eOyJFYZ8+e1cTFxWn+97//aWrXrq0ZOXJk6WPS09M17u7umldeeUUTFRWl+fnnnzX29vaar7/+WtHYjd2VK1ceGOWwd+9eOfJqzpw5mosXL2pOnTqlCQsLkyPqcnNzFY3X1I6FGBHXqlUrTZcuXTSnT5/WnDx5UtOuXTtNz549FY3VVI/H/TgaS7ljsW/fPnmOCA8P1yQlJZXe0tLSFI1V37HPThUSnVxF5+SuXbvK66liQrT33nuv3Bw6Li4usiwv5ngRo4Hef/99zJw5k9m6AsSlwx9//FFeUhQj53r37i2Pobi0ZWdnp3R4JkVU2EQ5383NTfYTEeX6Zs2ayf9PRKZMXMoSVegFCxbIiQVLbpwP7PHYZ4eIiIiMGis7REREZNSY7BAREZFRY7JDRERERo3JDhERERk1JjtERERk1JjsEBERkVFjskNERERGjckOERERGTUmO0RERGTUmOwQGalnn30W77777iPb1fGaStO3eIhIGUx2iJ7g1VdfhZmZGRYuXFhuu1hDS2w3FJs2bcK8efOUDsOgMFkiMg5MdogqwNbWFosWLcLdu3d1ts/CwkJUJ1dXVzg5OcEUVPdnq2+vb6ixEVUVJjtEFdCjRw94eHjIlYYfpaCgABMmTECdOnVkctSpUyecOHGiXJXg7bfflpUCsZp3WFhY6fbx48fL7TVr1oS7uztWr16NnJwcjB49WiYovr6++P3338u9nliNXbxGjRo1UKtWLfTr1w9xcXEVqlJcvXpVVqXuv4nHCGq1Wr7XRo0ayRXfQ0JCsGHDhnL7E/GNHDkSjo6OctXlTz755ImfozbvtSLv81Gf7f1+++03uLi44Icffnji+xQVvT///BOfffZZ6ecjPrdHva+Hvf6TPkfxc1BQkLxPvDfxeyY+i4r8Pgne3t5Yvnx5uW0tWrTA7NmzHxubiGvx4sXys7axsUGDBg3w8ccflz6nIsf/cbHfr+T3bePGjXIVe/Gctm3bIiEhAYcOHUL79u1hb2+P7t27Iz09/aH7IKoUseo5ET3aqFGjNAMHDtRs2rRJY2trq0lMTJTbN2/erCn7X2jChAmaevXqaXbs2KGJjo6Wz6tZs6YmLS1N3t+1a1eNo6OjZvLkyZoLFy7IW8l2Jycnzbx58zQXL16U/1pYWGj69OmjWbVqldw2btw4Ta1atTQ5OTmlr7dhwwbNxo0bNZcuXdKcOXNG079/f01QUJBGpVKV7vedd94pfXzZdnFxsSYpKan0Jp4v9j9jxgx5/0cffaTx9/fX7Ny5UxMXF6f59ttvNTY2NpoDBw6U7k/E1KBBA82ePXs0ERERmn79+sn3UfY176fNe33S+3zSZ1sSzw8//CBfe9u2baXPe9z7TE9P13To0EHz+uuvl35O4nN71Pt62Os/bv83b97UWFpaapYtW6a5cuWK/AxXrlypycrKqtDvk9CwYUPNp59+Wi6WkJAQzaxZsx4b25QpU+S+1q5dq7l8+bLm0KFDmtWrV1focxGeFPv9tmzZIv+vdO/eXb7W6dOnNV5eXprOnTtrnn/+ec2JEyc0f//9tzzuYp9EusZkh6iCyY7Qvn17zZgxYx5IdrKzszVWVlbyhFqisLBQnqwWL15cetJp2bLlA/sX2zt16lTaFidUBwcHzSuvvFK6TZxoxWsdPXr0kXGmpKTIx0RGRj4x2SkrLy9P065dO5msiAQiPz9fY29vrzly5Ei5x40dO1YzYsQI+bM4qVlbW2t++eWX0vvFSdjOzu6JyU5l3+v97/NJn62IZ8WKFRoXF5dyyVpF3uejPrOHvc79r/+k/Z86dUq+j6tXrz6wv4r8Pj1NslM2tszMTJm4lE1uniZu4XGxP8zs2bM1rq6umtTU1NJtL7/8ssbb27tcAt+7d2+ZiJUQidbWrVsr9BpEj2NZuboQkWkR/Xa6deuGSZMmldsuLqsUFRXhmWeeKd1mZWWF0NBQnD9/vnRb69atH7rf4ODg0p8tLCzkZQFxiaCEuNwj3L59u3TbpUuXMHPmTBw7dgypqany0oMgLg0EBgZW+D2NGTMGWVlZ+OOPP2Bubo7Lly8jNzcXPXv2fKCvR8uWLUvfr2i3a9euXJ8gPz+/J77e077Xir7PR3224nKL2Nfhw4flpZMSFXmfT+P+13/S/sWlIXHZRrx3cWmpV69eGDZsmLy8V9HfJ21iE88Xl8jEaz9MRT6Xx8X+MOfOncPgwYPlsS4hjt/w4cPl5auy2wYOHFjaFpczxe/mgAEDnvo9E5XFZIfoKYj+BuKPe3h4uOzT8bQcHBweul2cyMoS/RvKbisZ9VVyohf69++Phg0byj4v9erVk/eJk//TdED96KOPsGvXLhw/fry083J2dnZp/xZPT89yjxf9Oyrrad9rRd/noz5bcYI+ffo0vvnmG7Rp06Z0/7p+n/e//pP2LxI9kWAeOXIEu3fvxhdffIHp06fLpK6iRHIqKvRliSTpcbGJ/jKPU5HP5XGxi34+9zt79qz8P3N/AvTee++VtvPz8xEbGysTKUH0l5oxY4ZMkNatW4e//vrrkceY6EnYQZnoKYkh6Nu2bcPRo0dLt/n4+MDa2lpWD8qedESH0ubNm+s8hrS0NHli+PDDD+U37GbNmj31SDHRWXTu3Ln45ZdfZPwlRLzipCa+ZYsOrGVvXl5e8jHi8SJBKXtiFq9/8eJFvXufItb9+/dj69atsnP007xPcUxVKpVWsVdk/yLxEtWbOXPm4MyZM/L1Nm/eXOHfp9q1ayMpKam0nZmZiStXrjw2riZNmsiEZ+/evVrH/bjY7ydiEh2Uy1bLRIwZGRnltkVGRsrEraTK17VrV1kFFEmV2D8THaoMVnaInpL4Y/zSSy/h888/L90m/hCPGzcOkydPlpdzxOgWMdpFXA4YO3aszmMQlwvEN95Vq1bJkVDixDRt2rQKPz8qKkqOpJo6dSoCAgKQnJwst4sTlohfXKYT37pFFUWMAhInJnHidXZ2xqhRo+QILPG+xPsVcYgRQ+Kbvag06NP7LNG0aVOZ8IiRSZaWlnIEk6hkPel9itFOIqETJ2vxnsVnU9H3+KT9+/v7y4RDXAISn594nZSUFJnQVfT3SVxSXbt2rax+idFq4nKfqLo8jhjZJY77lClT5PEWCYt43ejoaLnvinwuItZHxX4/UcERMZW95CgqPeJ9iYpd2W0iyROfcwlxvMUxIKosJjtEWhAVEVFav7/iI04Or7zyiuxnIC6ZiEtEj+rHUBnihPvzzz/LocniJCL6yojkq2To+JOcPHlSnjjFZSxxKyG+TR84cEBOPiiqBmL4cXx8vDyRtmrVCh988EHpY5csWSIveYgTrThBvv/++/KkqE/vsyzx3H379snnipOvGCr/pPcpTvri5C6qHXl5ebIi8TQn38ftXyQOBw8elImXqH6IE7+IqU+fPhX+fRKXhkRMYji+GFIvXu9JlR1BXB4SSZ9Ijm7evCkTyTfffLNCcQtPiv3+ZEd89iLJKrvt/n5RYlvJJSzh+vXr8rIlkS6YiV7KOtkTERGRjohKkkim1q9fr3QoZATYZ4eIiPSOqOSJqpK4bBwTE6N0OGTgWNkhIiIio8bKDhERERk1JjtERERk1JjsEBERkVFjskNERERGjckOERERGTUmO0RERGTUmOwQERGRUWOyQ0REREaNyQ4REREZNSY7REREZNSY7BARERGM2f8HtherjUrbka0AAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Linear perfect foresight consumption function:\")\n", "mMin = PFexample.solution[0].mNrmMin\n", "plt.xlabel(r\"Normalized market resources $m_t$\")\n", "plt.ylabel(r\"Normalized consumption $c_t$\")\n", "plt.ylim(0.0, 0.5)\n", "plot_funcs(PFexample.solution[0].cFunc, mMin, mMin + 10.0)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlabel(r\"Normalized market resources $m_t$\")\n", "plt.ylabel(r\"Value function $v(m_t)$\")\n", "plt.ylim(-5000, 0.0)\n", "plot_funcs(PFexample.solution[0].vFunc, mMin + 0.1, mMin + 10.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An element of `solution` also includes the (normalized) marginal value function `vPfunc`, and the lower and upper bounds of the marginal propensity to consume (MPC) `MPCmin` and `MPCmax`. Note that with a linear consumption function, the MPC is constant, so its lower and upper bound are identical.\n", "\n", "### Liquidity constrained perfect foresight example\n", "\n", "Without an artificial borrowing constraint, a perfect foresight consumer is free to borrow against the PDV of his entire future stream of labor income -- his \"human wealth\" `hNrm` -- and he will consume a constant proportion of his total wealth (market resources plus human wealth). If we introduce an artificial borrowing constraint, both of these features vanish. In the cell below, we define a parameter dictionary that prevents the consumer from borrowing *at all*, create and solve a new instance of `PerfForesightConsumerType` with it, and then plot its consumption function." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "editable": true, "pycharm": { "name": "#%%\n" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "LiqConstrDict = copy(PerfForesightDict)\n", "LiqConstrDict[\"BoroCnstArt\"] = 0.0 # Set the artificial borrowing constraint to zero\n", "\n", "LiqConstrExample = PerfForesightConsumerType(**LiqConstrDict)\n", "LiqConstrExample.cycles = 0 # Make this type be infinite horizon\n", "LiqConstrExample.solve()\n", "\n", "plt.xlabel(r\"Normalized market resources $m_t$\")\n", "plt.ylabel(r\"Normalized consumption $c_t$\")\n", "plt.ylim(0.0, 2.0)\n", "plot_funcs(LiqConstrExample.solution[0].cFunc, 0.0, 10.0)" ] }, { "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, "source": [ "At this time, the value function for a perfect foresight consumer with an artificial borrowing constraint is not computed nor included as part of its `solution`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulating the perfect foresight consumer model\n", "\n", "Suppose we wanted to simulate many consumers who share the parameter values that we passed to `PerfForesightConsumerType` -- an *ex ante* homogeneous *type* of consumers. To do this, our instance would have to know *how many* agents there are of this type, as well as their initial levels of assets $a_t$ and permanent income $P_t$.\n", "\n", "### Setting simulation parameters\n", "\n", "Let's fill in this information by passing another dictionary to `PFexample` with simulation parameters. The table below lists the parameters that an instance of `PerfForesightConsumerType` needs in order to successfully simulate its model using the `simulate` method.\n", "\n", "| Description | Code | Example value |\n", "| :---: | --- | --- |\n", "| Number of consumers of this type | `AgentCount` | $10000$ |\n", "| Number of periods to simulate | `T_sim` | $120$ |\n", "| Mean of initial log (normalized) assets | `kLogInitMean` | $-6.0$ |\n", "| Stdev of initial log (normalized) assets | `kLogInitStd` | $1.0$ |\n", "| Mean of initial log permanent income | `pLogInitMean` | $0.0$ |\n", "| Stdev of initial log permanent income | `pLogInitStd` | $0.0$ |\n", "| Aggregrate productivity growth factor | `PermGroFacAgg` | $1.0$ |\n", "| Age after which consumers are automatically killed | `T_age` | $None$ |\n", "\n", "We have specified the model so that initial assets and permanent income are both distributed lognormally, with mean and standard deviation of the underlying normal distributions provided by the user.\n", "\n", "The parameter `PermGroFacAgg` exists for compatibility with more advanced models that employ aggregate productivity shocks; it can simply be set to 1.\n", "\n", "In infinite horizon models, it might be useful to prevent agents from living extraordinarily long lives through a fortuitous sequence of mortality shocks. We have thus provided the option of setting `T_age` to specify the maximum number of periods that a consumer can live before they are automatically killed (and replaced with a new consumer with initial state drawn from the specified distributions). This can be turned off by setting it to `None`.\n", "\n", "The cell below puts these parameters into a dictionary, then gives them to `PFexample`. Note that all of these parameters *could* have been passed as part of the original dictionary; we omitted them above for simplicity." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "SimulationParams = {\n", " \"AgentCount\": 10000, # Number of agents of this type\n", " \"T_sim\": 120, # Number of periods to simulate\n", " \"kLogInitMean\": -6.0, # Mean of log initial assets\n", " \"kLogInitStd\": 1.0, # Standard deviation of log initial assets\n", " \"pLogInitMean\": 0.0, # Mean of log initial permanent income\n", " \"pLogInitStd\": 0.0, # Standard deviation of log initial permanent income\n", " \"PermGroFacAgg\": 1.0, # Aggregate permanent income growth factor\n", " \"T_age\": None, # Age after which simulated agents are automatically killed\n", "}\n", "\n", "PFexample.assign_parameters(**SimulationParams)" ] }, { "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, "source": [ "To generate simulated data, we need to specify which variables we want to track the \"history\" of for this instance. To do so, we set the `track_vars` attribute of our `PerfForesightConsumerType` instance to be a list of strings with the simulation variables we want to track.\n", "\n", "In this model, valid arguments to `track_vars` include `mNrm`, `cNrm`, `aNrm`, and `pLvl`. Because this model has no idiosyncratic shocks, our simulated data will be quite boring.\n", "\n", "### Generating simulated data\n", "\n", "Before simulating, the `initialize_sim` method must be invoked. This resets our instance back to its initial state, drawing a set of initial `aNrm` and `pLvl` values from the specified distributions and storing them in the attributes `aNrmNow_init` and `pLvlNow_init`. It also resets this instance's internal random number generator, so that the same initial states will be set every time `initialize_sim` is called. In models with non-trivial shocks, this also ensures that the same sequence of shocks will be generated on every simulation run.\n", "\n", "Finally, the `simulate` method can be called." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "PFexample.track_vars = [\"mNrm\"]\n", "PFexample.initialize_sim()\n", "PFexample.simulate()" ] }, { "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, "source": [ "Each simulation variable `X` named in `track_vars` will have the *history* of that variable for each agent stored in the attribute `X_hist` as an array of shape `(T_sim, AgentCount)`. To see that the simulation worked as intended, we can plot the mean of $m_t$ in each simulated period:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1))\n", "plt.xlabel(\"Time\")\n", "plt.ylabel(\"Mean normalized market resources\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, "source": [ "A perfect foresight consumer can borrow against the PDV of his future income -- his human wealth -- and thus as time goes on, our simulated agents approach the (very negative) steady state level of $m_t$ while being steadily replaced with consumers with roughly $m_t=1$.\n", "\n", "The slight wiggles in the plotted curve are due to consumers randomly dying and being replaced; their replacement will have an initial state drawn from the distributions specified by the user. To see the current distribution of ages, we can look at the attribute `t_age`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "editable": true, "pycharm": { "name": "#%%\n" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = PFexample.AgentCount\n", "F = np.linspace(0.0, 1.0, N)\n", "plt.plot(np.sort(PFexample.t_age), F)\n", "plt.xlabel(\"Current age of consumers\")\n", "plt.ylabel(\"Cumulative distribution\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "incorrectly_encoded_metadata": "pycharm= [markdown] {\"name\": \"#%% md\\n\"}" }, "source": [ "The distribution is (discretely) exponential, with a point mass at 120 with consumers who have survived since the beginning of the simulation.\n", "\n", "One might wonder why HARK requires users to call `initialize_sim` before calling `simulate`: Why doesn't `simulate` just call `initialize_sim` as its first step? We have broken up these two steps so that users can simulate some number of periods, change something in the environment, and then resume the simulation.\n", "\n", "When called with no argument, `simulate` will simulate the model for `T_sim` periods. The user can optionally pass an integer specifying the number of periods to simulate (which should not exceed `T_sim`).\n", "\n", "In the cell below, we simulate our perfect foresight consumers for 80 periods, then seize a bunch of their assets (dragging their wealth even more negative), then simulate for the remaining 40 periods.\n", "\n", "The `state_prev` attribute of an `AgentType` stores the values of the model's state variables in the _previous_ period of the simulation." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "editable": true, "pycharm": { "name": "#%%\n" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PFexample.initialize_sim()\n", "PFexample.simulate(80)\n", "PFexample.state_now[\n", " \"aNrm\"\n", "] += -5.0 # Adjust all simulated consumers' assets downward by 5\n", "PFexample.simulate(40)\n", "\n", "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1))\n", "plt.xlabel(\"Time\")\n", "plt.ylabel(\"Mean normalized market resources\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "cell_metadata_filter": "collapsed,code_folding,name,title,incorrectly_encoded_metadata,pycharm", "cell_metadata_json": true, "formats": "ipynb,py:percent" }, "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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 4 }