From c0f95e6be3d4990af5e634160f5dea2983bd5599 Mon Sep 17 00:00:00 2001 From: sb Date: Thu, 13 Feb 2020 12:36:58 -0500 Subject: [PATCH 1/4] for #87 Gentle Buffer Stock intro, paring down parameters; cosmetic edits; discrete distribution display --- ...tle-Intro-To-HARK-Buffer-Stock-Model.ipynb | 188 +++++++++++------- ...Gentle-Intro-To-HARK-Buffer-Stock-Model.py | 106 +++++----- 2 files changed, 165 insertions(+), 129 deletions(-) diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb index ca79171e..baba1ae0 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb @@ -6,7 +6,38 @@ "source": [ "# A Gentle Introduction to Buffer Stock Saving \n", "\n", - "This notebook explores the behavior of a consumer identical to the perfect foresight consumer described in [Gentle-Intro-To-HARK-PerfForesightCRRA](https://econ-ark.org/materials/Gentle-Intro-To-HARK-PerfForesightCRRA) except that now the model incorporates income uncertainty.\n", + "This notebook explores the behavior of a consumer identical to the perfect foresight consumer described in [Gentle-Intro-To-HARK-PerfForesightCRRA](https://econ-ark.org/materials/Gentle-Intro-To-HARK-PerfForesightCRRA) except that now the model incorporates income uncertainty." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# This cell has just a bit of initial setup. You can click the triangle to the left to expand it.\n", + "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n", + "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n", + "# The most common problem beginners have is to execute a cell before all its predecessors\n", + "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import numpy as np\n", + "import HARK \n", + "from time import clock\n", + "from copy import deepcopy\n", + "mystr = lambda number : \"{:.4f}\".format(number)\n", + "from HARK.utilities import plotFuncs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changes to the model\n", "\n", "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$. Moreover, lenders set a limit on borrowing: The ratio of end-of-period assets $A_t$ to permanent income $P_t$ must be less greater than $\\underline{a} \\leq 0$. As with the perfect foresight problem, this model can be framed in terms of _normalized_ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\n", "\n", @@ -19,23 +50,13 @@ "U(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", "\\end{eqnarray*}\n", "\n", - "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create a $\\texttt{IndShockConsumerType}$ instance, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock distributions $\\{F_t\\}$. It's easy enough to pick a borrowing constraint-- say, $\\underline{a} = 0$ so that the consumer cannot borrow at all.\n", - "\n", - "Computers are discrete devices; even if somehow we knew with certainty that the distributions of the transitory and permanent shocks were, say, continuously lognormally distributed, in order to be represented on a computer those distributions would need to be approximated by a finite and discrete set of points. A large literature in numerical computation explores ways to construct such approximations; probably the easiest example to understand is the equiprobable approximation, in which the continuous distribution is represented by a set of $N$ outcomes that are equally likely to occur. \n", - "\n", - "In the case of a single variable (say, the permanent shock $\\psi$), and when the number of equiprobable points is, say, 5, the procedure is to construct a list: $psi_{0}$ is the mean value of the continuous $\\psi$ given that the draw of $\\psi$ is in the bottom 20 percent of the distribution of the continuous $\\psi$. $\\\\psi_{1}$ is the mean value of $\\psi$ given that the draw is between the 20th and 40th percentiles, and so on. The expectation of some expression $f(\\psi)$ can be very quickly calculated by:\n", - "\n", - "$$ \n", - "\\mathbb{E}_{t}[f(\\psi)] \\approx (1/N) \\sum_{i=0}^{N-1} f(\\psi_{i})\n", - "$$" + "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create a $\\texttt{IndShockConsumerType}$ instance, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock distributions $\\{F_t\\}$. It's easy enough to pick a borrowing constraint-- say, $\\underline{a} = 0$ so that the consumer cannot borrow at all." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In principle, any smooth multivariate continuous distribution can be approximated to an arbitrary degree of accuracy with a fine enough matrix of points and their corresponding probailities. This is, in fact, the fundamental way that HARK represents uncertainty: By a specifying a multidimensional array containing joint probabilities of the realizations of the shocks.\n", - "\n", "The simplest assumption (and therefore the default choice in $\\texttt{IndShockConsumerType}$) is that the transitory and permanent shocks are independent. The permanent shock is assumed to be lognormal, while the transitory shock has two components: A probability $\\wp$ that the consumer is unemployed, in which case $\\theta=\\underline{\\theta}$, and a probability $(1-\\wp)$ of a shock that is a lognormal with a mean chosen so that $\\mathbb{E}_{t}[\\theta_{t+n}]=1$.\n", "\n", "The $\\texttt{IndShockConsumerType}$ inherits all of the parameters of the original $\\texttt{PerfForesightConsumerType}$ class. Given the assumptions above, we need to specify the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n", @@ -54,31 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "# This cell has just a bit of initial setup. You can click the triangle to the left to expand it.\n", - "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n", - "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n", - "# The most common problem beginners have is to execute a cell before all its predecessors\n", - "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import numpy as np\n", - "import HARK \n", - "from time import clock\n", - "from copy import deepcopy\n", - "mystr = lambda number : \"{:.4f}\".format(number)\n", - "from HARK.utilities import plotFuncs" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "code_folding": [ 0, @@ -90,11 +87,6 @@ "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n", "\n", "IndShockDictionary = {\n", - " 'CRRA': 2.5, # The dictionary includes our original parameters...\n", - " 'Rfree': 1.03,\n", - " 'DiscFac': 0.96,\n", - " 'LivPrb': [0.98],\n", - " 'PermGroFac': [1.01],\n", " 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. \n", " 'PermShkCount': 7,\n", " 'TranShkStd': [0.1],\n", @@ -102,32 +94,22 @@ " 'UnempPrb': 0.05,\n", " 'IncUnemp': 0.3,\n", " 'BoroCnstArt': 0.0,\n", - " 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets.\n", - " 'aXtraMax': 50., # Don't worry about these for now\n", - " 'aXtraNestFac': 3,\n", - " 'aXtraCount': 48,\n", - " 'aXtraExtra': [None],\n", - " 'vFuncBool': False, # These booleans indicate whether the value function should be calculated\n", - " 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them.\n", - " 'aNrmInitMean' : -10.,\n", - " 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets\n", - " 'pLvlInitMean' : 0.0, # and permanent income for agents at \"birth\". They are only relevant in\n", - " 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them.\n", - " 'PermGroFacAgg' : 1.0,\n", - " 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to\n", - " 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping\n", - " 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now.\n", - " 'T_age' : None,\n", - " 'T_cycle' : 1,\n", - " 'cycles' : 0,\n", - " 'AgentCount': 10000,\n", - " 'tax_rate':0.0,\n", "}\n", " \n", "# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to\n", "# think about them for now." ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", + "IndShockExample = IndShockConsumerType(**IndShockDictionary)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -135,14 +117,61 @@ "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discretizing probability distributions\n", + "\n", + "Computers are discrete devices; even if somehow we knew with certainty that the distributions of the transitory and permanent shocks were, say, continuously lognormally distributed, in order to be represented on a computer those distributions would need to be approximated by a finite and discrete set of points. A large literature in numerical computation explores ways to construct such approximations; probably the easiest example to understand is the equiprobable approximation, in which the continuous distribution is represented by a set of $N$ outcomes that are equally likely to occur. \n", + "\n", + "In the case of a single variable (say, the permanent shock $\\psi$), and when the number of equiprobable points is, say, 5, the procedure is to construct a list: $psi_{0}$ is the mean value of the continuous $\\psi$ given that the draw of $\\psi$ is in the bottom 20 percent of the distribution of the continuous $\\psi$. $\\\\psi_{1}$ is the mean value of $\\psi$ given that the draw is between the 20th and 40th percentiles, and so on. The expectation of some expression $f(\\psi)$ can be very quickly calculated by:\n", + "\n", + "$$ \n", + "\\mathbb{E}_{t}[f(\\psi)] \\approx (1/N) \\sum_{i=0}^{N-1} f(\\psi_{i})\n", + "$$\n", + "\n", + "In principle, any smooth multivariate continuous distribution can be approximated to an arbitrary degree of accuracy with a fine enough matrix of points and their corresponding probailities. This is, in fact, the fundamental way that HARK represents uncertainty: By a specifying a multidimensional array containing joint probabilities of the realizations of the shocks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scatterplot below shows how the discretized probability distribution is represented in HARK. The lognormal distribution is represented by a set of equiprobable point masses." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", - "IndShockExample = IndShockConsumerType(**IndShockDictionary)" + "plt.scatter(IndShockExample.PermShkDstn[0][1],\n", + " IndShockExample.PermShkDstn[0][0])\n", + "plt.xlabel(\"Value\")\n", + "plt.ylabel(\"Probability Mass\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the problem" ] }, { @@ -154,9 +183,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+jSXyw7TYlIhnUyhfAcObIYtIg05dbGpC/7rFppI6a7EpCT766JkEBGsty7cfYvbSbPIKj5GWEMdT09MYqcWmJIipwMXvbc4v4cHFdYtN9ekYw/wZI5g0UItNiajAxW/lFR5j9pIslm0/RMc2ETxwyWAuS9diUyL/pgIXv3PqYlO3T0zmurO02JTIqfSKEL9x6mJTV52eyE3j+tGhdYTT0UT8kgpcHFdV6+LVdQU8Wb/Y1EVDu3H7ecn06hDjdDQRv6YCF8ecutjUGf06cM/k/gzuocWmRDyhAhdHfJxbzKwlmWzdV0b/rm156drBnJ0Ur8kSkUZQgUuzOnmxqe5xUTw2dSgXD+2uxaZEmkAFLs3i5MWm4qLD+MP5/ZkxuheRYVpsSqSpVODiUyUV1cxblceidfkYA/9vbF9uPKcvsVFabErkx1KBi0+cutjUz0f05NaJSXSNjXI6mkiLoQIXr6p1uXlr814eW5HDobIqJvTvxF2TU0nWYlMiXqcCF684dbGp4QlxPDktjVG9tdiUiK+owOVH25x/hAcXZ9YtNhUfw/wZaUwa2EUjgSI+pgKXJttZdIzZS7JZsu0g8a0j+MtPBzF1ZE/CtNiUSLNQgUujFZVX8cTKXF7bUEBEaAi3TUjm+rN6ExOhHyeR5qRXnHjseHUtC9fu4tk1O6isdTNtVE9uGZ9MxzZabErECSpwaVCty82bm+omS4rKq5g0sDN3TU6lb8fWTkcTCWoqcPle1lpWZBYyKyOTHUUVjOjVjmemp5GubcxE/IIKXL7TloIjPLg4iw27S+onS7SNmYi/UYHLt+wuruDhpdl88PUB4luHa7JExI+pwAWAw8fqJkteXV9AWKsQbhmfxA1n96G1JktE/JZenUHuRLWL5z/eyfw1OzlR42LqyJ7cOj6JTm0jnY4mIg1QgQepWpebtz/fy6PL69YsOW9A3WRJv06aLBEJFCrwIGOtZXV2IbMyssg5VLdmybwr0hipyRKRgNNggRtjegIvA50BCyyw1j7u62DifV/uOcoDizNZv6uE3vExPDM9jcmDtGaJSKDy5Aq8FrjdWvu5MaYNsNkYs9xau93H2cRL8g/XTZa8/9UBOsSE8+eLB3L5qARNlogEuAYL3Fp7ADhQ/3W5MSYT6A6owP1cSUU1T67KZdG6fEJDQrh5XD9uOLsPbSK1G45IS9Coe+DGmERgOLD+O/7dTGAmQEJCgheiSVOduhvO1JE9uXVCMp01WSLSonhc4MaY1sDbwK3W2rJT/721dgGwACA9Pd16LaF4zOW2dZMly3I4WFbJhP6duXtyCknaDUekRfKowI0xYdSV96vW2nd8G0kay1rLhzlFzFqcRfahcob2jOPxy4dxWp8OTkcTER/yZArFAM8DmdbaR30fSRrj672lPJiRyac7DtOrQzRPT09jiiZLRIKCJ1fgZwBXAl8bY76o/97vrLWLfRdLGrKn5DgPL83mn1/up31MOH+6aCDTRiUQHqrJEpFg4ckUyseALuf8xJGKauatzuPlz3bTKsTwm3P78ctzNFkiEoz0ScwAUVnj4q+f7uap1XlUVNVyWXrdZEmXWE2WiAQrFbifc7st736xjzlLs9lfWsn41E7cPSWVZE2WiAQ9Fbgf+3RHMQ8szmTrvjIGd4/lkcuGcXpfTZaISB0VuB/KKyxnVkYWKzIL6R4XxeOXD+PCId0ICdFbESLyDRW4Hykqr2Luihze2LiH6LBW3DMllWvGJBIZ1srpaCLih1TgfuDfmyo88+EOqmrdXDm6FzeN60eH1hFORxMRP6YCd5DbbXlnS90blAfLKpk0sDN3T06lT0dtqiAiDVOBO+STvGLu/yCT7QfKGNojliemDWdUb22qICKeU4E3s5xD5Ty4OJPV2UV0j4viiWnDuWBwV71BKSKNpgJvJoXllTy2PJe/bSwgJiKU3/0klatO1xuUItJ0KnAfO15dy8K1u5i/ZgfVtW6uHpPIzeOSaBcT7nQ0EQlwKnAfcbktb2/eyyPLszlUVsWUQV24a3IqveNjnI4mIi2ECtwH1uYWcf8HmWQdLGdYzzieuiKNdO36LiJepgL3orzCYzywOJNVWYX0bB/FvCuGc/7grlqbW0R8QgXuBUcqqnl8Zd3mwVFhrbh3SirXnJFIRKjeoBQR31GB/wg1LjevfJbP4ytzKa+sYdqoBG6bmEy8PkEpIs1ABd4E1lpWZRVy/weZ7Cyu4KykeH5/fn9Su7R1OpqIBBEVeCNlHSzjL+9n8nFeMX06xvDCNemcm9JJ97lFpNmpwD1UfKyKR5fn8MaGAtpEhnHfhQOYMboXYa20B6WIOEMF3oCqWhcvfrKbp1blcaLGxVWnJ3LrhCTiovVBHBFxlgr8e1hrWbL1IA9mZFFQcpzxqZ343fn96auVAkXET6jAv8O2/aX86V/b2bCrhJTObXjlulGcldTR6VgiIt+iAj9JSUU1jyzL5vUNBcRFh3P/JYOYmt6TUN3nFhE/pAIHal1uXttQwCPLcjhWVcvVYxK5dXwysdFhTkcTEfleQV/gn+04zJ/+tY2sg+WM6duB/3/RQJI7t3E6lohIg4K2wPcdPcEDizP54KsDdI+L4pnpaUwe1EXz3CISMIKuwKtqXSxYs5OnPszDWrh1QhK/PLsvUeFat0REAkuDBW6MeQG4ACi01g7yfSTfWZtbxB/f28au4gqmDOrC78/vT4920U7HEhFpEk+uwP8KzANe9m0U3zlUVsmf39/O+18dILFDNC9fO4qzkzUWKCKBrcECt9Z+ZIxJ9H0U76t1uXnps3weW55DtcvNbROS+eU5fbQPpYi0CF67B26MmQnMBEhISPDWYZtsc/4R/vDuVjIPlHFOckf+5+KB9Oqg7cxEpOXwWoFbaxcACwDS09Ott47bWKUnapiVkcXrGwroGhvJ/BlpTBqo6RIRaXla1BTKkq0H+ON72yg+VsX1Z/bmtonJxES0qP9EEZH/0yLa7WBpJX98byvLth9iYLe2PH/1SAb3iHU6loiIT3kyRvg6MBaIN8bsBe6z1j7v62CeemNDAfd/kEm1y829U1K57szeWrtERIKCJ1Mo05ojSFPMX7ODWRlZjOnbgQcuGUxivN6kFJHgEbC3UF75bDezMrK4aGg3Hps6jFYhepNSRIJLQN5reGvzXv77vW1M6N+ZRy4bqvIWkaAUcAX+wVcHuOutLzkrKZ55VwzXnpQiErQCqv1WZR3ilje2MKJXO569coQ+USkiQS1gCvzTvGJuXPQ5/bu25flrRhIdHrC370VEvCIgCnxz/hGuf3kTvTvE8PK1o2gbqZ1yRET8vsC37ivlmhc30KlNBK9cP4p2MeFORxIR8Qt+XeB5heVc9cIG2kaG8eoNo+nUJtLpSCIifsNvC7zg8HGmL1xPqxDDoutPo3tclNORRET8il8W+IHSE1yxcB1VtW4WXXcavfUJSxGR/+B3BV5UXsX059ZTeryGV649jZQu2iFeROS7+NUs3tHj1Vz5/HoOlFby8nWjtKKgiMgP8Jsr8GNVtVz94kZ2FlXw3FXpjExs73QkERG/5hdX4CeqXVz7141s3VfK/BkjODMp3ulIIiJ+z/Er8KpaFzcu2szG3SU8NnUYEwd0djqSiEhAcLTAa11ubn59C2tyinjoZ0O4aGg3J+OIiAQUxwrc7bbc+dZXLN12iPsuHMBlI3s6FUVEJCA5UuDWWv7w3lb+sWUfd05K4Rdn9HYihohIQGv2ArfWcv8Hmby2voBfje3Lr8/t19wRRERahGYv8Lkrcln48S6uGZPInZNSmvuPFxFpMZq1wBd8tIPHV+by8xE9+OMFAzBGW6GJiDRVsxX4K+vyeWBxFhcM6cqsS4cQon0sRUR+lGYp8Hc+38t/v7uV8amdtIO8iIiX+LzAM74+wB1//5IxfTvw1PQ0bUIsIuIlPm3T1dmF3PzGFoYntOO5q9K1CbGIiBf5rMA/23GYG1/ZTHLnNrxwzUhiIvxi2RURkRbDowI3xkw2xmQbY/KMMfc09Pjj1S6uf2kjCe2jeeW604iN0ibEIiLe1mCBG2NaAU8BU4ABwDRjzIAfes7u4gri20Sw6PrTaK9NiEVEfMKTK/BRQJ61dqe1thp4A7j4Bw8aYnj1+tPo3FabEIuI+IonBd4d2HPS7/fWf+9bjDEzjTGbjDGb4kKq6NEu2lsZRUTkO3jtTUxr7QJrbbq1Nr1LJ23IICLia54U+D7g5LVee9R/T0REHORJgW8EkowxvY0x4cDlwD99G0tERBrS4HC2tbbWGPMbYCnQCnjBWrvN58lEROQHefTpGmvtYmCxj7OIiEgjaGESEZEApQIXEQlQKnARkQClAhcRCVDGWuv9gxpTDmR7/cCBKR4odjqEH9B5+IbOxTd0Lr6RYq1t05gn+GqN12xrbbqPjh1QjDGbdC50Hk6mc/ENnYtvGGM2NfY5uoUiIhKgVOAiIgHKVwW+wEfHDUQ6F3V0Hr6hc/ENnYtvNPpc+ORNTBER8T3dQhERCVAqcBGRAOXVAm/s5sctlTGmpzFmtTFmuzFmmzHmFqczOc0Y08oYs8UY877TWZxkjIkzxrxljMkyxmQaY053OpNTjDG31b8+thpjXjfGBM0ejMaYF4wxhcaYrSd9r70xZrkxJrf+13YNHcdrBd6UzY9bsFrgdmvtAGA08OsgPhf/dguQ6XQIP/A4sMRamwoMJUjPiTGmO3AzkG6tHUTdUtWXO5uqWf0VmHzK9+4BVlprk4CV9b//Qd68Am/05sctlbX2gLX28/qvy6l7kf7HPqLBwhjTAzgfWOh0FicZY2KBs4HnAay11dbao86mclQoEGWMCQWigf0O52k21tqPgJJTvn0x8FL91y8BP23oON4scI82Pw42xphEYDiw3tkkjpoL3AW4nQ7isN5AEfBi/e2khcaYGKdDOcFauw+YAxQAB4BSa+0yZ1M5rrO19kD91weBzg09QW9i+pAxpjXwNnCrtbbM6TxOMMZcABR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "IndShockExample.solve()\n", "plotFuncs(IndShockExample.solution[0].cFunc,0.,10.)" @@ -177,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -196,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "code_folding": [], "lines_to_next_cell": 2 @@ -212,9 +254,9 @@ "formats": "ipynb,py:percent" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.7 econ-ark", "language": "python", - "name": "python3" + "name": "econ-ark" }, "language_info": { "codemirror_mode": { @@ -226,7 +268,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.7.5" } }, "nbformat": 4, diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py index af36c1f0..950c3fd2 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py @@ -6,17 +6,36 @@ # extension: .py # format_name: percent # format_version: '1.2' -# jupytext_version: 1.2.1 +# jupytext_version: 1.2.4 # kernelspec: -# display_name: Python 3 +# display_name: Python 3.7 econ-ark # language: python -# name: python3 +# name: econ-ark # --- # %% [markdown] # # A Gentle Introduction to Buffer Stock Saving # # This notebook explores the behavior of a consumer identical to the perfect foresight consumer described in [Gentle-Intro-To-HARK-PerfForesightCRRA](https://econ-ark.org/materials/Gentle-Intro-To-HARK-PerfForesightCRRA) except that now the model incorporates income uncertainty. + +# %% {"code_folding": []} +# This cell has just a bit of initial setup. You can click the triangle to the left to expand it. +# Click the "Run" button immediately above the notebook in order to execute the contents of any cell +# WARNING: Each cell in the notebook relies upon results generated by previous cells +# The most common problem beginners have is to execute a cell before all its predecessors +# If you do this, you can restart the kernel (see the "Kernel" menu above) and start over +# %matplotlib inline +import matplotlib.pyplot as plt + +import numpy as np +import HARK +from time import clock +from copy import deepcopy +mystr = lambda number : "{:.4f}".format(number) +from HARK.utilities import plotFuncs + +# %% [markdown] +# ## Changes to the model # # Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\theta_t$ and a completely permanent shock $\psi_t$. Moreover, lenders set a limit on borrowing: The ratio of end-of-period assets $A_t$ to permanent income $P_t$ must be less greater than $\underline{a} \leq 0$. As with the perfect foresight problem, this model can be framed in terms of _normalized_ variables, e.g. $m_t \equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory). # @@ -30,18 +49,8 @@ # \end{eqnarray*} # # HARK represents agents with this kind of problem as instances of the class $\texttt{IndShockConsumerType}$. To create a $\texttt{IndShockConsumerType}$ instance, we must specify the same set of parameters as for a $\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\underline{a}$ and a sequence of income shock distributions $\{F_t\}$. It's easy enough to pick a borrowing constraint-- say, $\underline{a} = 0$ so that the consumer cannot borrow at all. -# -# Computers are discrete devices; even if somehow we knew with certainty that the distributions of the transitory and permanent shocks were, say, continuously lognormally distributed, in order to be represented on a computer those distributions would need to be approximated by a finite and discrete set of points. A large literature in numerical computation explores ways to construct such approximations; probably the easiest example to understand is the equiprobable approximation, in which the continuous distribution is represented by a set of $N$ outcomes that are equally likely to occur. -# -# In the case of a single variable (say, the permanent shock $\psi$), and when the number of equiprobable points is, say, 5, the procedure is to construct a list: $psi_{0}$ is the mean value of the continuous $\psi$ given that the draw of $\psi$ is in the bottom 20 percent of the distribution of the continuous $\psi$. $\\psi_{1}$ is the mean value of $\psi$ given that the draw is between the 20th and 40th percentiles, and so on. The expectation of some expression $f(\psi)$ can be very quickly calculated by: -# -# $$ -# \mathbb{E}_{t}[f(\psi)] \approx (1/N) \sum_{i=0}^{N-1} f(\psi_{i}) -# $$ # %% [markdown] -# In principle, any smooth multivariate continuous distribution can be approximated to an arbitrary degree of accuracy with a fine enough matrix of points and their corresponding probailities. This is, in fact, the fundamental way that HARK represents uncertainty: By a specifying a multidimensional array containing joint probabilities of the realizations of the shocks. -# # The simplest assumption (and therefore the default choice in $\texttt{IndShockConsumerType}$) is that the transitory and permanent shocks are independent. The permanent shock is assumed to be lognormal, while the transitory shock has two components: A probability $\wp$ that the consumer is unemployed, in which case $\theta=\underline{\theta}$, and a probability $(1-\wp)$ of a shock that is a lognormal with a mean chosen so that $\mathbb{E}_{t}[\theta_{t+n}]=1$. # # The $\texttt{IndShockConsumerType}$ inherits all of the parameters of the original $\texttt{PerfForesightConsumerType}$ class. Given the assumptions above, we need to specify the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary: @@ -57,31 +66,10 @@ # | $\mho$ | Unemployment probability | $\texttt{UnempPrb}$ | 0.05 | # | $\underline{\theta}$ | Transitory shock when unemployed | $\texttt{IncUnemp}$ | 0.3 | -# %% {"code_folding": []} -# This cell has just a bit of initial setup. You can click the triangle to the left to expand it. -# Click the "Run" button immediately above the notebook in order to execute the contents of any cell -# WARNING: Each cell in the notebook relies upon results generated by previous cells -# The most common problem beginners have is to execute a cell before all its predecessors -# If you do this, you can restart the kernel (see the "Kernel" menu above) and start over -# %matplotlib inline -import matplotlib.pyplot as plt - -import numpy as np -import HARK -from time import clock -from copy import deepcopy -mystr = lambda number : "{:.4f}".format(number) -from HARK.utilities import plotFuncs - # %% {"code_folding": [0, 2]} # This cell defines a parameter dictionary for making an instance of IndShockConsumerType. IndShockDictionary = { - 'CRRA': 2.5, # The dictionary includes our original parameters... - 'Rfree': 1.03, - 'DiscFac': 0.96, - 'LivPrb': [0.98], - 'PermGroFac': [1.01], 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. 'PermShkCount': 7, 'TranShkStd': [0.1], @@ -89,37 +77,43 @@ 'UnempPrb': 0.05, 'IncUnemp': 0.3, 'BoroCnstArt': 0.0, - 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets. - 'aXtraMax': 50., # Don't worry about these for now - 'aXtraNestFac': 3, - 'aXtraCount': 48, - 'aXtraExtra': [None], - 'vFuncBool': False, # These booleans indicate whether the value function should be calculated - 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them. - 'aNrmInitMean' : -10., - 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets - 'pLvlInitMean' : 0.0, # and permanent income for agents at "birth". They are only relevant in - 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them. - 'PermGroFacAgg' : 1.0, - 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to - 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping - 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now. - 'T_age' : None, - 'T_cycle' : 1, - 'cycles' : 0, - 'AgentCount': 10000, - 'tax_rate':0.0, } # Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to # think about them for now. +# %% +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +IndShockExample = IndShockConsumerType(**IndShockDictionary) + # %% [markdown] # As before, we need to import the relevant subclass of $\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function. +# %% [markdown] +# ## Discretizing probability distributions +# +# Computers are discrete devices; even if somehow we knew with certainty that the distributions of the transitory and permanent shocks were, say, continuously lognormally distributed, in order to be represented on a computer those distributions would need to be approximated by a finite and discrete set of points. A large literature in numerical computation explores ways to construct such approximations; probably the easiest example to understand is the equiprobable approximation, in which the continuous distribution is represented by a set of $N$ outcomes that are equally likely to occur. +# +# In the case of a single variable (say, the permanent shock $\psi$), and when the number of equiprobable points is, say, 5, the procedure is to construct a list: $psi_{0}$ is the mean value of the continuous $\psi$ given that the draw of $\psi$ is in the bottom 20 percent of the distribution of the continuous $\psi$. $\\psi_{1}$ is the mean value of $\psi$ given that the draw is between the 20th and 40th percentiles, and so on. The expectation of some expression $f(\psi)$ can be very quickly calculated by: +# +# $$ +# \mathbb{E}_{t}[f(\psi)] \approx (1/N) \sum_{i=0}^{N-1} f(\psi_{i}) +# $$ +# +# In principle, any smooth multivariate continuous distribution can be approximated to an arbitrary degree of accuracy with a fine enough matrix of points and their corresponding probailities. This is, in fact, the fundamental way that HARK represents uncertainty: By a specifying a multidimensional array containing joint probabilities of the realizations of the shocks. + +# %% [markdown] +# The scatterplot below shows how the discretized probability distribution is represented in HARK. The lognormal distribution is represented by a set of equiprobable point masses. + # %% -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType -IndShockExample = IndShockConsumerType(**IndShockDictionary) +plt.scatter(IndShockExample.PermShkDstn[0][1], + IndShockExample.PermShkDstn[0][0]) +plt.xlabel("Value") +plt.ylabel("Probability Mass") +plt.show() + +# %% [markdown] +# ## Solving the problem # %% [markdown] # Now we can solve our new agent's problem just like before, using the $\texttt{solve}$ method. From f22c03c8d3c407be3967d63f54665e8ef7a7fb35 Mon Sep 17 00:00:00 2001 From: sb Date: Fri, 14 Feb 2020 17:05:14 -0500 Subject: [PATCH 2/4] adding notes about inheritance and object inspection into Gentle Buffer Stock notebook --- ...tle-Intro-To-HARK-Buffer-Stock-Model.ipynb | 230 +++++++++++++++++- ...Gentle-Intro-To-HARK-Buffer-Stock-Model.py | 32 ++- 2 files changed, 252 insertions(+), 10 deletions(-) diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb index baba1ae0..3a922f6f 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb @@ -48,9 +48,7 @@ "m_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", "(\\psi_t,\\theta_t) \\sim F_{t}, &\\qquad& \\mathbb{E} [F_{\\psi t}] = [F_{\\theta t}] = 1, \\\\\n", "U(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", - "\\end{eqnarray*}\n", - "\n", - "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create a $\\texttt{IndShockConsumerType}$ instance, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock distributions $\\{F_t\\}$. It's easy enough to pick a borrowing constraint-- say, $\\underline{a} = 0$ so that the consumer cannot borrow at all." + "\\end{eqnarray*}" ] }, { @@ -73,6 +71,13 @@ "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create a $\\texttt{IndShockConsumerType}$ instance, we only need to define an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock distributions $\\{F_t\\}$. It's easy enough to pick a borrowing constraint-- say, $\\underline{a} = 0$ so that the consumer cannot borrow at all." + ] + }, { "cell_type": "code", "execution_count": 2, @@ -100,13 +105,176 @@ "# think about them for now." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Though we can override them if we like, it's simpler to use default values provided by HARK. The `PerfForesightConsumerType` class has many of these attributes already.\n", + "\n", + "You can see all the **attributes** of an object in Python by using the `dir()` command. You can see that many of the model variables are now attributes of this object, along with many other attributes that are outside the scope of this tutorial." + ] + }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['AgentCount',\n", + " 'BoroCnstArt',\n", + " 'CRRA',\n", + " 'DiscFac',\n", + " 'LivPrb',\n", + " 'MaxKinks',\n", + " 'PermGroFac',\n", + " 'PermGroFacAgg',\n", + " 'RNG',\n", + " 'Rfree',\n", + " 'T_age',\n", + " 'T_cycle',\n", + " '__call__',\n", + " '__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'aNrmInitMean',\n", + " 'aNrmInitStd',\n", + " 'addToTimeInv',\n", + " 'addToTimeVary',\n", + " 'assignParameters',\n", + " 'cFunc_terminal_',\n", + " 'checkConditions',\n", + " 'checkElementsOfTimeVaryAreLists',\n", + " 'checkRestrictions',\n", + " 'clearHistory',\n", + " 'cycles',\n", + " 'delFromTimeInv',\n", + " 'delFromTimeVary',\n", + " 'distance',\n", + " 'getAvg',\n", + " 'getControls',\n", + " 'getMortality',\n", + " 'getPostStates',\n", + " 'getRfree',\n", + " 'getShocks',\n", + " 'getStates',\n", + " 'initializeSim',\n", + " 'makeShockHistory',\n", + " 'pLvlInitMean',\n", + " 'pLvlInitStd',\n", + " 'postSolve',\n", + " 'poststate_vars',\n", + " 'poststate_vars_',\n", + " 'preSolve',\n", + " 'pseudo_terminal',\n", + " 'quiet',\n", + " 'readShocks',\n", + " 'read_shocks',\n", + " 'resetRNG',\n", + " 'seed',\n", + " 'shock_vars',\n", + " 'shock_vars_',\n", + " 'simBirth',\n", + " 'simDeath',\n", + " 'simOnePeriod',\n", + " 'simulate',\n", + " 'solution_terminal',\n", + " 'solution_terminal_',\n", + " 'solve',\n", + " 'solveOnePeriod',\n", + " 'timeFlip',\n", + " 'timeFwd',\n", + " 'timeReport',\n", + " 'timeRev',\n", + " 'time_flow',\n", + " 'time_inv',\n", + " 'time_inv_',\n", + " 'time_vary',\n", + " 'time_vary_',\n", + " 'tolerance',\n", + " 'track_vars',\n", + " 'unpackcFunc',\n", + " 'updateSolutionTerminal',\n", + " 'vFunc_terminal_',\n", + " 'verbose']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n", + "\n", + "pfc = PerfForesightConsumerType()\n", + "dir(pfc)" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], + "source": [ + "`IndShockConsumerType` is a **superclass** of `PerfForesightConsumerType`. This means that it builds on the functionality of the other type. You can find the superclasses of a type in Python using the `__bases__` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType,)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", + "\n", + "IndShockConsumerType.__bases__" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ "IndShockExample = IndShockConsumerType(**IndShockDictionary)" ] }, @@ -143,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -167,6 +335,13 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that this distribution was created when the `IndShockConsumerType` object was initialized, but it was not an attribute you gave it directly." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -183,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -204,6 +379,45 @@ "plotFuncs(IndShockExample.solution[0].cFunc,0.,10.)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This solution was generated by running a **method** that is an attribute of the `IndShockExample` object. Many methods in Python have a description, called its **documentation**, that will tell you what it does and what its arguments are. You can read the documentation for methods and other attributes in HARK with the built-in Python `help()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method solve in module HARK.core:\n", + "\n", + "solve(verbose=False) method of HARK.ConsumptionSaving.ConsIndShockModel.IndShockConsumerType instance\n", + " Solve the model for this instance of an agent type by backward induction.\n", + " Loops through the sequence of one period problems, passing the solution\n", + " from period t+1 to the problem for period t.\n", + " \n", + " Parameters\n", + " ----------\n", + " verbose : boolean\n", + " If True, solution progress is printed to screen.\n", + " \n", + " Returns\n", + " -------\n", + " none\n", + "\n" + ] + } + ], + "source": [ + "help(IndShockExample.solve)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -219,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": { "code_folding": [], "lines_to_next_cell": 2 diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py index 950c3fd2..5aa4c64c 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py @@ -47,8 +47,6 @@ # (\psi_t,\theta_t) \sim F_{t}, &\qquad& \mathbb{E} [F_{\psi t}] = [F_{\theta t}] = 1, \\ # U(c) &=& \frac{c^{1-\rho}}{1-\rho}. # \end{eqnarray*} -# -# HARK represents agents with this kind of problem as instances of the class $\texttt{IndShockConsumerType}$. To create a $\texttt{IndShockConsumerType}$ instance, we must specify the same set of parameters as for a $\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\underline{a}$ and a sequence of income shock distributions $\{F_t\}$. It's easy enough to pick a borrowing constraint-- say, $\underline{a} = 0$ so that the consumer cannot borrow at all. # %% [markdown] # The simplest assumption (and therefore the default choice in $\texttt{IndShockConsumerType}$) is that the transitory and permanent shocks are independent. The permanent shock is assumed to be lognormal, while the transitory shock has two components: A probability $\wp$ that the consumer is unemployed, in which case $\theta=\underline{\theta}$, and a probability $(1-\wp)$ of a shock that is a lognormal with a mean chosen so that $\mathbb{E}_{t}[\theta_{t+n}]=1$. @@ -66,6 +64,9 @@ # | $\mho$ | Unemployment probability | $\texttt{UnempPrb}$ | 0.05 | # | $\underline{\theta}$ | Transitory shock when unemployed | $\texttt{IncUnemp}$ | 0.3 | +# %% [markdown] +# HARK represents agents with this kind of problem as instances of the class $\texttt{IndShockConsumerType}$. To create a $\texttt{IndShockConsumerType}$ instance, we only need to define an artificial borrowing constraint $\underline{a}$ and a sequence of income shock distributions $\{F_t\}$. It's easy enough to pick a borrowing constraint-- say, $\underline{a} = 0$ so that the consumer cannot borrow at all. + # %% {"code_folding": [0, 2]} # This cell defines a parameter dictionary for making an instance of IndShockConsumerType. @@ -82,8 +83,26 @@ # Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to # think about them for now. +# %% [markdown] +# Though we can override them if we like, it's simpler to use default values provided by HARK. The `PerfForesightConsumerType` class has many of these attributes already. +# +# You can see all the **attributes** of an object in Python by using the `dir()` command. You can see that many of the model variables are now attributes of this object, along with many other attributes that are outside the scope of this tutorial. + +# %% {"jupyter": {"outputs_hidden": true}} +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType + +pfc = PerfForesightConsumerType() +dir(pfc) + +# %% [markdown] +# `IndShockConsumerType` is a **superclass** of `PerfForesightConsumerType`. This means that it builds on the functionality of the other type. You can find the superclasses of a type in Python using the `__bases__` attribute. + # %% from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType + +IndShockConsumerType.__bases__ + +# %% IndShockExample = IndShockConsumerType(**IndShockDictionary) # %% [markdown] @@ -112,6 +131,9 @@ plt.ylabel("Probability Mass") plt.show() +# %% [markdown] +# Notice that this distribution was created when the `IndShockConsumerType` object was initialized, but it was not an attribute you gave it directly. + # %% [markdown] # ## Solving the problem @@ -122,6 +144,12 @@ IndShockExample.solve() plotFuncs(IndShockExample.solution[0].cFunc,0.,10.) +# %% [markdown] +# This solution was generated by running a **method** that is an attribute of the `IndShockExample` object. Many methods in Python have a description, called its **documentation**, that will tell you what it does and what its arguments are. You can read the documentation for methods and other attributes in HARK with the built-in Python `help()` function. + +# %% +help(IndShockExample.solve) + # %% [markdown] # ## Changing Constructed Attributes # From 7376d901bb5190fe08329765007e6c8f87ab15d3 Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 17 Feb 2020 09:27:06 -0500 Subject: [PATCH 3/4] removing unneeded notebook %matplotlib line magic --- notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb | 1 - notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py | 1 - 2 files changed, 2 deletions(-) diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb index 3a922f6f..855d9a22 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb @@ -22,7 +22,6 @@ "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n", "# The most common problem beginners have is to execute a cell before all its predecessors\n", "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", - "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import numpy as np\n", diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py index 5aa4c64c..03bdbf77 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py @@ -24,7 +24,6 @@ # WARNING: Each cell in the notebook relies upon results generated by previous cells # The most common problem beginners have is to execute a cell before all its predecessors # If you do this, you can restart the kernel (see the "Kernel" menu above) and start over -# %matplotlib inline import matplotlib.pyplot as plt import numpy as np From 71205f91e5bda07dcfad5a84d7611e2baa02bf0c Mon Sep 17 00:00:00 2001 From: sb Date: Mon, 17 Feb 2020 10:03:50 -0500 Subject: [PATCH 4/4] adding section on backwards induction and inspecting the solution --- ...tle-Intro-To-HARK-Buffer-Stock-Model.ipynb | 207 +++++++++++++++++- ...Gentle-Intro-To-HARK-Buffer-Stock-Model.py | 47 +++- 2 files changed, 241 insertions(+), 13 deletions(-) diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb index 855d9a22..79485662 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb @@ -98,6 +98,8 @@ " 'UnempPrb': 0.05,\n", " 'IncUnemp': 0.3,\n", " 'BoroCnstArt': 0.0,\n", + " \n", + " 'cycles' : 0 # infinite time horizon -- see below\n", "}\n", " \n", "# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to\n", @@ -116,12 +118,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -341,6 +338,169 @@ "Notice that this distribution was created when the `IndShockConsumerType` object was initialized, but it was not an attribute you gave it directly." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backwards Induction\n", + "\n", + "HARK will solve this problem using _backwards induction_.\n", + "\n", + "It will derive a solution for each period ($t$) by choosing the optimal policy mapping from market resources $m$ to consumption $c$. This function will be stored in a variable named `cFunc`.\n", + "\n", + "Backwards induction requires a \"terminal\" (last, final) period to work backwards from. Our `IndShockExample` has been initialized with a terminal solution. There are many functions wrapped together in the solution object, which is of type `ConsumerSolution`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IndShockExample.solution_terminal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The consumption function `cFunc` is define by _linear interpolation_.\n", + "It is defined by a series of $(x,y)$ points on a grid; the value of the function for any $x$ is the $y$ determined by the line between the nearest defined gridpoints.\n", + "You can see below that in the terminal period, $c = m$; the agent consumes all available resources." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(IndShockExample.solution_terminal.cFunc.x_list,\n", + " IndShockExample.solution_terminal.cFunc.y_list,\n", + " color='k')\n", + "plt.scatter(IndShockExample.solution_terminal.cFunc.x_list,\n", + " IndShockExample.solution_terminal.cFunc.y_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The solution also has a representation of a `value function`, the value `v(m)` as a function of available market resources. Because the agent consumes all their resources in the last period, the value function for the terminal solution looks just like the CRRA utility function." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sb/projects/econ-ark/HARK/HARK/utilities.py:141: RuntimeWarning: divide by zero encountered in reciprocal\n", + " return( c**(1.0 - gam) / (1.0 - gam) )\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0,1,20)\n", + "plt.plot(x,\n", + " IndShockExample.solution_terminal.vFunc(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If these are the consumption and value functions for the _last_ period, what are the functions for the _first_ period?\n", + "\n", + "Recall that we are solving this problem on the _infinite time horizon_; in other words, there is no \"first period\"." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IndShockExample.cycles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What we will do instead is perform backwards induction until the consumption and value functions _converge_. We will see that this derived consumption function looks quite different from the terminal solution." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -357,12 +517,23 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -387,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -432,9 +603,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "OtherExample = deepcopy(IndShockExample) # Make a copy so we can compare consumption functions\n", "OtherExample.PermShkStd = [0.2] # Double permanent income risk (note that it's a one element list)\n", @@ -451,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": { "code_folding": [], "lines_to_next_cell": 2 diff --git a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py index 03bdbf77..b0bad846 100644 --- a/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py +++ b/notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.py @@ -77,6 +77,8 @@ 'UnempPrb': 0.05, 'IncUnemp': 0.3, 'BoroCnstArt': 0.0, + + 'cycles' : 0 # infinite time horizon -- see below } # Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to @@ -87,7 +89,7 @@ # # You can see all the **attributes** of an object in Python by using the `dir()` command. You can see that many of the model variables are now attributes of this object, along with many other attributes that are outside the scope of this tutorial. -# %% {"jupyter": {"outputs_hidden": true}} +# %% from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType pfc = PerfForesightConsumerType() @@ -133,6 +135,49 @@ # %% [markdown] # Notice that this distribution was created when the `IndShockConsumerType` object was initialized, but it was not an attribute you gave it directly. +# %% [markdown] +# ## Backwards Induction +# +# HARK will solve this problem using _backwards induction_. +# +# It will derive a solution for each period ($t$) by choosing the optimal policy mapping from market resources $m$ to consumption $c$. This function will be stored in a variable named `cFunc`. +# +# Backwards induction requires a "terminal" (last, final) period to work backwards from. Our `IndShockExample` has been initialized with a terminal solution. There are many functions wrapped together in the solution object, which is of type `ConsumerSolution`. + +# %% +IndShockExample.solution_terminal + +# %% [markdown] +# The consumption function `cFunc` is define by _linear interpolation_. +# It is defined by a series of $(x,y)$ points on a grid; the value of the function for any $x$ is the $y$ determined by the line between the nearest defined gridpoints. +# You can see below that in the terminal period, $c = m$; the agent consumes all available resources. + +# %% +plt.plot(IndShockExample.solution_terminal.cFunc.x_list, + IndShockExample.solution_terminal.cFunc.y_list, + color='k') +plt.scatter(IndShockExample.solution_terminal.cFunc.x_list, + IndShockExample.solution_terminal.cFunc.y_list) + +# %% [markdown] +# The solution also has a representation of a `value function`, the value `v(m)` as a function of available market resources. Because the agent consumes all their resources in the last period, the value function for the terminal solution looks just like the CRRA utility function. + +# %% +x = np.linspace(0,1,20) +plt.plot(x, + IndShockExample.solution_terminal.vFunc(x)) + +# %% [markdown] +# If these are the consumption and value functions for the _last_ period, what are the functions for the _first_ period? +# +# Recall that we are solving this problem on the _infinite time horizon_; in other words, there is no "first period". + +# %% +IndShockExample.cycles + +# %% [markdown] +# What we will do instead is perform backwards induction until the consumption and value functions _converge_. We will see that this derived consumption function looks quite different from the terminal solution. + # %% [markdown] # ## Solving the problem