{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", " \n", " \"QuantEcon\"\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Consumption and Tax Smoothing with Complete and Incomplete Markets\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents\n", "\n", "- [Consumption and Tax Smoothing with Complete and Incomplete Markets](#Consumption-and-Tax-Smoothing-with-Complete-and-Incomplete-Markets) \n", " - [Overview](#Overview) \n", " - [Background](#Background) \n", " - [Model 1 (Complete Markets)](#Model-1-%28Complete-Markets%29) \n", " - [Model 2 (One-Period Risk Free Debt Only)](#Model-2-%28One-Period-Risk-Free-Debt-Only%29) \n", " - [Example: Tax Smoothing with Complete Markets](#Example:-Tax-Smoothing-with-Complete-Markets) \n", " - [Linear State Space Version of Complete Markets Model](#Linear-State-Space-Version-of-Complete-Markets-Model) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "This lecture describes two types of consumption-smoothing and tax-smoothing models\n", "\n", "- One is in the **complete markets** tradition of Lucas and Stokey [[LS83]](../zreferences.html#lucasstokey1983). \n", "- The other is in the **incomplete markets** tradition of Hall [[Hal78]](../zreferences.html#hall1978) and Barro [[Bar79]](../zreferences.html#barro1979). \n", "- Complete markets* allow a consumer or government to buy or sell claims contingent on all possible states of the world. \n", "- Incomplete markets* allow a consumer or government to buy or sell only a limited set of securities, often only a single risk-free security. \n", "\n", "\n", "Hall [[Hal78]](../zreferences.html#hall1978) and Barro [[Bar79]](../zreferences.html#barro1979) both assumed that the only asset that can be traded is a risk-free one period bond.\n", "\n", "Hall assumed an exogenous stochastic process of nonfinancial income and\n", "an exogenous gross interest rate on one period risk-free debt that equals\n", "$ \\beta^{-1} $, where $ \\beta \\in (0,1) $ is also a consumer’s\n", "intertemporal discount factor.\n", "\n", "Barro [[Bar79]](../zreferences.html#barro1979) made an analogous assumption about the risk-free interest\n", "rate in a tax-smoothing model that we regard as isomorphic to Hall’s\n", "consumption-smoothing model.\n", "\n", "We maintain Hall and Barro’s assumption about the interest rate when we describe an\n", "incomplete markets version of our model.\n", "\n", "In addition, we extend their assumption about the interest rate to an appropriate counterpart that we use in a “complete markets” model in the style of\n", "Lucas and Stokey [[LS83]](../zreferences.html#lucasstokey1983).\n", "\n", "While we are equally interested in consumption-smoothing and tax-smoothing\n", "models, for the most part we focus explicitly on consumption-smoothing\n", "versions of these models.\n", "\n", "But for each version of the consumption-smoothing model there is a natural tax-smoothing counterpart obtained simply by\n", "\n", "- relabeling consumption as tax collections and nonfinancial income as government expenditures \n", "- relabeling the consumer’s debt as the government’s *assets* \n", "\n", "\n", "For elaborations on this theme, please see [Optimal Savings II: LQ Techniques](perm_income_cons.html) and later parts of this lecture.\n", "\n", "We’ll consider two closely related alternative assumptions about the consumer’s\n", "exogenous nonfinancial income process (or in the tax-smoothing\n", "interpretation, the government’s exogenous expenditure process):\n", "\n", "- that it obeys a finite $ N $ state Markov chain (setting $ N=2 $ most of the time) \n", "- that it is described by a linear state space model with a continuous\n", " state vector in $ {\\mathbb R}^n $ driven by a Gaussian vector iid shock\n", " process \n", "\n", "\n", "We’ll spend most of this lecture studying the finite-state Markov specification, but will briefly treat the linear state space specification before concluding." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relationship to Other Lectures\n", "\n", "This lecture can be viewed as a followup to [Optimal Savings II: LQ Techniques](perm_income_cons.html) and a warm up for a model of tax smoothing described in [opt_tax_recur](../dynamic_programming_squared/opt_tax_recur.html).\n", "\n", "Linear-quadratic versions of the Lucas-Stokey tax-smoothing model are described in [lqramsey](../dynamic_programming_squared/lqramsey.html).\n", "\n", "The key differences between those lectures and this one are\n", "\n", "- Here the decision maker takes all prices as exogenous, meaning that his decisions do not affect them. \n", "- In [lqramsey](../dynamic_programming_squared/lqramsey.html) and [opt_tax_recur](../dynamic_programming_squared/opt_tax_recur.html), the decision maker – the government in the case of these lectures – recognizes that his decisions affect prices. \n", "\n", "\n", "So these later lectures are partly about how the government should manipulate prices of government debt." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "Outcomes in consumption-smoothing (or tax-smoothing) models emerge from two\n", "sources:\n", "\n", "- a decision maker – a consumer in the consumption-smoothing model or\n", " a government in the tax-smoothing model – who wants to maximize an\n", " intertemporal objective function that expresses its preference for\n", " paths of consumption (or tax collections) that are *smooth* in the\n", " sense of not varying across time and Markov states \n", "- a set of trading opportunities that allow the optimizer to transform\n", " a possibly erratic nonfinancial income (or government expenditure)\n", " process into a smoother consumption (or tax collections) process by\n", " purchasing or selling financial securities \n", "\n", "\n", "In the complete markets version of the model, each period the consumer\n", "can buy or sell one-period ahead state-contingent securities whose\n", "payoffs depend on next period’s realization of the Markov state.\n", "\n", "In the two-state Markov chain case, there are two such securities each period.\n", "\n", "In an $ N $ state Markov state version of the model, $ N $ such securities are traded each period.\n", "\n", "These state-contingent securities are commonly called Arrow securities, after [Kenneth Arrow](https://en.wikipedia.org/wiki/Kenneth_Arrow) who first theorized about them.\n", "\n", "In the incomplete markets version of the model, the consumer can buy and sell only one security each period, a risk-free bond with gross return $ \\beta^{-1} $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finite State Markov Income Process\n", "\n", "In each version of the consumption-smoothing model, nonfinancial income is governed by a two-state Markov chain (it’s easy to generalize this to an $ N $ state Markov chain).\n", "\n", "In particular, the *state of the world* is given by $ s_t $ that follows\n", "a Markov chain with transition probability matrix\n", "\n", "$$\n", "P_{ij} = \\mathbb P \\{s_{t+1} = \\bar s_j \\,|\\, s_t = \\bar s_i \\}\n", "$$\n", "\n", "Nonfinancial income $ \\{y_t\\} $ obeys\n", "\n", "$$\n", "y_t =\n", "\\begin{cases}\n", " \\bar y_1 & \\quad \\text{if } s_t = \\bar s_1 \\\\\n", " \\bar y_2 & \\quad \\text{if } s_t = \\bar s_2\n", "\\end{cases}\n", "$$\n", "\n", "A consumer wishes to maximize\n", "\n", "\n", "\n", "$$\n", "\\mathbb E\n", "\\left[\n", " \\sum_{t=0}^\\infty \\beta^t u(c_t)\n", "\\right]\n", "\\quad\n", "\\text{where} \\quad\n", "u(c_t) = - (c_t -\\gamma)^2\n", "\\quad \\text{and} \\quad\n", " 0 < \\beta < 1 \\tag{1}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Remark About Isomorphism\n", "\n", "We can regard these as Barro [[Bar79]](../zreferences.html#barro1979) tax-smoothing models if we set\n", "$ c_t = T_t $ and $ G_t = y_t $, where $ T_t $ is total tax\n", "collections and $ \\{G_t\\} $ is an exogenous government expenditures\n", "process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Market Structure\n", "\n", "The two models differ in how effectively the market structure allows the\n", "consumer to transfer resources across time and Markov states, there\n", "being more transfer opportunities in the complete markets setting than\n", "in the incomplete markets setting.\n", "\n", "Watch how these differences in opportunities affect\n", "\n", "- how smooth consumption is across time and Markov states \n", "- how the consumer chooses to make his levels of indebtedness behave\n", " over time and across Markov states " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 1 (Complete Markets)\n", "\n", "At each date $ t \\geq 0 $, the consumer trades **one-period ahead\n", "Arrow securities**.\n", "\n", "We assume that prices of these securities are exogenous to the consumer\n", "(or in the tax-smoothing version of the model, to the government).\n", "\n", "*Exogenous* means that they are unaffected by the decision maker.\n", "\n", "In Markov state $ s_t $ at time $ t $, one unit of consumption\n", "in state $ s_{t+1} $ at time $ t+1 $ costs $ q(s_{t+1} \\,|\\, s_t) $ units of the time $ t $ consumption good.\n", "\n", "At time $ t=0 $, the consumer starts with an inherited level of debt\n", "due at time $ 0 $ of $ b_0 $ units of time $ 0 $ consumption\n", "goods.\n", "\n", "The consumer’s budget constraint at $ t \\geq 0 $ in Markov\n", "state $ s_t $ is\n", "\n", "$$\n", "c_t + b_t\n", "\\leq y(s_t) +\n", "\\sum_j q(\\bar s_j \\,|\\, s_t ) \\, b_{t+1}(\\bar s_j \\,|\\, s_t)\n", "$$\n", "\n", "where $ b_t $ is the consumer’s one-period debt that falls due at time $ t $ and $ b_{t+1}(\\bar s_j\\,|\\, s_t) $ are the consumer’s time\n", "$ t $ sales of the time $ t+1 $ consumption good in Markov state $ \\bar s_j $, a source of time $ t $ revenues.\n", "\n", "An analogue of Hall’s assumption that the one-period risk-free gross\n", "interest rate is $ \\beta^{-1} $ is\n", "\n", "\n", "\n", "$$\n", "q(\\bar s_j \\,|\\, \\bar s_i) = \\beta P_{ij} \\tag{2}\n", "$$\n", "\n", "To understand this, observe that in state $ \\bar s_i $ it costs $ \\sum_j q(\\bar s_j \\,|\\, \\bar s_i) $ to purchase one unit of consumption next period *for sure*, i.e., meaning no matter what state of the world occurs at $ t+1 $.\n", "\n", "Hence the implied price of a risk-free claim on one unit of consumption next\n", "period is\n", "\n", "$$\n", "\\sum_j q(\\bar s_j \\,|\\, \\bar s_i) = \\sum_j \\beta P_{ij} = \\beta\n", "$$\n", "\n", "This confirms that [(2)](#equation-cs-2) is a natural analogue of Hall’s assumption about the\n", "risk-free one-period interest rate.\n", "\n", "First-order necessary conditions for maximizing the consumer’s expected utility are\n", "\n", "$$\n", "\\beta \\frac{u'(c_{t+1})}{u'(c_t) } \\mathbb P\\{s_{t+1}\\,|\\, s_t \\}\n", " = q(s_{t+1} \\,|\\, s_t)\n", "$$\n", "\n", "or, under our assumption [(2)](#equation-cs-2) on Arrow security prices,\n", "\n", "\n", "\n", "$$\n", "c_{t+1} = c_t \\tag{3}\n", "$$\n", "\n", "Thus, our consumer sets $ c_t = \\bar c $ for all $ t \\geq 0 $ for some value $ \\bar c $ that it is our job now to determine.\n", "\n", "**Guess:** We’ll make the plausible guess that\n", "\n", "\n", "\n", "$$\n", "b_{t+1}(\\bar s_j \\,|\\, s_t = \\bar s_i) = b(\\bar s_j) ,\n", " \\quad i=1,2; \\;\\; j= 1,2 \\tag{4}\n", "$$\n", "\n", "so that the amount borrowed today turns out to depend only on *tomorrow’s* Markov state. (Why is this is a plausible guess?).\n", "\n", "To determine $ \\bar c $, we shall pursue the implications of the consumer’s budget constraints in each Markov state today and our guess [(4)](#equation-eq-guess) about the consumer’s debt level choices.\n", "\n", "For $ t \\geq 1 $, these imply\n", "\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", " \\bar c + b(\\bar s_1) & = y(\\bar s_1) + q(\\bar s_1\\,|\\, \\bar s_1) b(\\bar s_1) + q(\\bar s_2 \\,|\\, \\bar s_1) b(\\bar s_2) \\cr\n", " \\bar c + b(\\bar s_2) & = y(\\bar s_2) + q(\\bar s_1\\,|\\, \\bar s_2) b(\\bar s_1) + q(\\bar s_2 \\,|\\, \\bar s_2) b(\\bar s_2),\n", "\\end{aligned} \\tag{5}\n", "$$\n", "\n", "or\n", "\n", "$$\n", "\\begin{bmatrix}\n", " b(\\bar s_1) \\cr b(\\bar s_2)\n", "\\end{bmatrix} +\n", "\\begin{bmatrix}\n", "\\bar c \\cr \\bar c\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", " y(\\bar s_1) \\cr y(\\bar s_2)\n", "\\end{bmatrix}\n", "+ \\beta\n", "\\begin{bmatrix}\n", " P_{11} & P_{12} \\cr P_{21} & P_{22}\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", " b(\\bar s_1) \\cr b(\\bar s_2)\n", "\\end{bmatrix}\n", "$$\n", "\n", "These are $ 2 $ equations in the $ 3 $ unknowns\n", "$ \\bar c, b(\\bar s_1), b(\\bar s_2) $.\n", "\n", "To get a third equation, we assume that at time $ t=0 $, $ b_0 $\n", "is the debt due; and we assume that at time $ t=0 $, the Markov\n", "state is $ \\bar s_1 $.\n", "\n", "Then the budget constraint at time $ t=0 $ is\n", "\n", "\n", "\n", "$$\n", "\\bar c + b_0 = y(\\bar s_1) + q(\\bar s_1 \\,|\\, \\bar s_1) b(\\bar s_1) + q(\\bar s_2\\,|\\,\\bar s_1) b(\\bar s_2) \\tag{6}\n", "$$\n", "\n", "If we substitute [(6)](#equation-cs-5) into the first equation of [(5)](#equation-cs-4a) and rearrange, we\n", "discover that\n", "\n", "\n", "\n", "$$\n", "b(\\bar s_1) = b_0 \\tag{7}\n", "$$\n", "\n", "We can then use the second equation of [(5)](#equation-cs-4a) to deduce the restriction\n", "\n", "\n", "\n", "$$\n", "y(\\bar s_1) - y(\\bar s_2) + [q(\\bar s_1\\,|\\, \\bar s_1) - q(\\bar s_1\\,|\\, \\bar s_2) - 1 ] b_0 +\n", "[q(\\bar s_2\\,|\\,\\bar s_1) + 1 - q(\\bar s_2 \\,|\\, \\bar s_2) ] b(\\bar s_2) = 0 , \\tag{8}\n", "$$\n", "\n", "an equation in the unknown $ b(\\bar s_2) $.\n", "\n", "Knowing $ b(\\bar s_1) $ and $ b(\\bar s_2) $, we can solve equation [(6)](#equation-cs-5) for the constant level of consumption $ \\bar c $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Key outcomes\n", "\n", "The preceding calculations indicate that in the complete markets version\n", "of our model, we obtain the following striking results:\n", "\n", "- The consumer chooses to make consumption perfectly constant across\n", " time and Markov states. \n", "\n", "\n", "We computed the constant level of consumption $ \\bar c $ and indicated how that level depends on the underlying specifications of preferences, Arrow securities prices, the stochastic process of exogenous nonfinancial income, and the initial debt level $ b_0 $\n", "\n", "- The consumer’s debt neither accumulates, nor decumulates, nor drifts.\n", " Instead the debt level each period is an exact function of the Markov\n", " state, so in the two-state Markov case, it switches between two\n", " values. \n", "- We have verified guess [(4)](#equation-eq-guess). \n", "\n", "\n", "We computed how one of those debt levels depends entirely on initial debt – it equals it – and how the other value depends on virtually all remaining parameters of the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code\n", "\n", "Here’s some code that, among other things, contains a function called consumption_complete().\n", "\n", "This function computes $ b(\\bar s_1), b(\\bar s_2), \\bar c $ as outcomes given a set of parameters, under the assumption of complete markets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide-output": true }, "outputs": [], "source": [ "using InstantiateFromURL\n", "# optionally add arguments to force installation: instantiate = true, precompile = true\n", "github_project(\"QuantEcon/quantecon-notebooks-julia\", version = \"0.8.0\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hide-output": false }, "outputs": [], "source": [ "using LinearAlgebra, Statistics\n", "using Parameters, Plots, QuantEcon, Random\n", "gr(fmt = :png);" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "consumption_incomplete (generic function with 1 method)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ConsumptionProblem = @with_kw (β = 0.96,\n", " y = [2.0, 1.5],\n", " b0 = 3.0,\n", " P = [0.8 0.2\n", " 0.4 0.6])\n", "\n", "function consumption_complete(cp)\n", "\n", " @unpack β, P, y, b0 = cp # Unpack\n", "\n", " y1, y2 = y # extract income levels\n", " b1 = b0 # b1 is known to be equal to b0\n", " Q = β * P # assumed price system\n", "\n", " # Using equation (7) calculate b2\n", " b2 = (y2 - y1 - (Q[1, 1] - Q[2, 1] - 1) * b1) / (Q[1, 2] + 1 - Q[2, 2])\n", "\n", " # Using equation (5) calculae c̄\n", " c̄ = y1 - b0 + ([b1 b2] * Q[1, :])[1]\n", "\n", " return c̄, b1, b2\n", "end\n", "\n", "function consumption_incomplete(cp; N_simul = 150)\n", "\n", " @unpack β, P, y, b0 = cp # unpack\n", "\n", " # for the simulation use the MarkovChain type\n", " mc = MarkovChain(P)\n", "\n", " # useful variables\n", " y = y\n", " v = inv(I - β * P) * y\n", "\n", " # simulate state path\n", " s_path = simulate(mc, N_simul, init=1)\n", "\n", " # store consumption and debt path\n", " b_path, c_path = ones(N_simul + 1), ones(N_simul)\n", " b_path[1] = b0\n", "\n", " # optimal decisions from (12) and (13)\n", " db = ((1 - β) * v - y) / β\n", "\n", " for (i, s) in enumerate(s_path)\n", " c_path[i] = (1 - β) * (v[s, 1] - b_path[i])\n", " b_path[i + 1] = b_path[i] + db[s, 1]\n", " end\n", "\n", " return c_path, b_path[1:end - 1], y[s_path], s_path\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s test by checking that $ \\bar c $ and $ b_2 $ satisfy the budget constraint" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hide-output": false }, "outputs": [ { "data": { "text/plain": [ "true" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cp = ConsumptionProblem()\n", "c̄, b1, b2 = consumption_complete(cp)\n", "debt_complete = [b1, b2]\n", "isapprox((c̄ + b2 - cp.y[2] - debt_complete' * (cp.β * cp.P)[2, :])[1], 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we’ll take the outcomes produced by this code – in particular the implied\n", "consumption and debt paths – and compare them with outcomes\n", "from an incomplete markets model in the spirit of Hall [[Hal78]](../zreferences.html#hall1978) and Barro [[Bar79]](../zreferences.html#barro1979) (and also, for those who love history, Gallatin (1807) [[Gal37]](../zreferences.html#gallatin))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 2 (One-Period Risk Free Debt Only)\n", "\n", "This is a version of the original models of Hall (1978) and Barro (1979)\n", "in which the decision maker’s ability to substitute intertemporally is\n", "constrained by his ability to buy or sell only one security, a risk-free\n", "one-period bond bearing a constant gross interest rate that equals\n", "$ \\beta^{-1} $.\n", "\n", "Given an initial debt $ b_0 $ at time $ 0 $, the\n", "consumer faces a sequence of budget constraints\n", "\n", "$$\n", "c_t + b_t = y_t + \\beta b_{t+1}, \\quad t \\geq 0\n", "$$\n", "\n", "where $ \\beta $ is the price at time $ t $ of a risk-free claim\n", "on one unit of time consumption at time $ t+1 $.\n", "\n", "First-order conditions for the consumer’s problem are\n", "\n", "$$\n", "\\sum_{j} u'(c_{t+1,j}) P_{ij} = u'(c_{t, i})\n", "$$\n", "\n", "For our assumed quadratic utility function this implies\n", "\n", "\n", "\n", "$$\n", "\\sum_j c_{t+1,j} P_{ij} = c_{t,i} , \\tag{9}\n", "$$\n", "\n", "which is Hall’s (1978) conclusion that consumption follows a random walk.\n", "\n", "As we saw in our first lecture on the [permanent income model](perm_income.html), this leads to\n", "\n", "\n", "\n", "$$\n", "b_t = \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j} - (1 -\\beta)^{-1} c_t \\tag{10}\n", "$$\n", "\n", "and\n", "\n", "\n", "\n", "$$\n", "c_t = (1-\\beta)\n", " \\left[\n", " \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j} - b_t\n", " \\right] . \\tag{11}\n", "$$\n", "\n", "Equation [(11)](#equation-cs-10) expresses $ c_t $ as a net interest rate factor $ 1 - \\beta $ times the sum\n", "of the expected present value of nonfinancial income $ \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j} $ and financial wealth $ -b_t $.\n", "\n", "Substituting [(11)](#equation-cs-10) into the one-period budget constraint and rearranging leads to\n", "\n", "\n", "\n", "$$\n", "b_{t+1} - b_t\n", "= \\beta^{-1} \\left[ (1-\\beta)\n", "\\mathbb E_t \\sum_{j=0}^\\infty\\beta^j y_{t+j} - y_t \\right] \\tag{12}\n", "$$\n", "\n", "Now let’s do a useful calculation that will yield a convenient expression for the key term $ \\mathbb E_t \\sum_{j=0}^\\infty\\beta^j y_{t+j} $ in our finite Markov chain setting.\n", "\n", "Define\n", "\n", "$$\n", "v_t := \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j y_{t+j}\n", "$$\n", "\n", "In our finite Markov chain setting, $ v_t = v(1) $ when $ s_t= \\bar s_1 $ and $ v_t = v(2) $ when $ s_t=\\bar s_2 $.\n", "\n", "Therefore, we can write\n", "\n", "$$\n", "\\begin{aligned}\n", " v(1) & = y(1) + \\beta P_{11} v(1) + \\beta P_{12} v(2)\n", " \\\\\n", " v(2) & = y(2) + \\beta P_{21} v(1) + \\beta P_{22} v(2)\n", "\\end{aligned}\n", "$$\n", "\n", "or\n", "\n", "$$\n", "\\vec v = \\vec y + \\beta P \\vec v\n", "$$\n", "\n", "where $ \\vec v = \\begin{bmatrix} v(1) \\cr v(2) \\end{bmatrix} $ and $ \\vec y = \\begin{bmatrix} y(1) \\cr y(2) \\end{bmatrix} $.\n", "\n", "We can also write the last expression as\n", "\n", "$$\n", "\\vec v = (I - \\beta P)^{-1} \\vec y\n", "$$\n", "\n", "In our finite Markov chain setting, from expression [(11)](#equation-cs-10), consumption at date $ t $ when debt is $ b_t $ and the Markov state today is $ s_t = i $ is evidently\n", "\n", "\n", "\n", "$$\n", "c(b_t, i) = (1 - \\beta) \\left( [(I - \\beta P)^{-1} \\vec y]_i - b_t \\right) \\tag{13}\n", "$$\n", "\n", "and the increment in debt is\n", "\n", "\n", "\n", "$$\n", "b_{t+1} - b_t = \\beta^{-1} [ (1- \\beta) v(i) - y(i) ] \\tag{14}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary of Outcomes\n", "\n", "In contrast to outcomes in the complete markets model, in the incomplete\n", "markets model\n", "\n", "- consumption drifts over time as a random walk; the level of\n", " consumption at time $ t $ depends on the level of debt that the\n", " consumer brings into the period as well as the expected discounted\n", " present value of nonfinancial income at $ t $ \n", "- the consumer’s debt drifts upward over time in response to low\n", " realizations of nonfinancial income and drifts downward over time in\n", " response to high realizations of nonfinancial income \n", "- the drift over time in the consumer’s debt and the dependence of\n", " current consumption on today’s debt level account for the drift over\n", " time in consumption " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Incomplete Markets Model\n", "\n", "The code above also contains a function called consumption_incomplete() that uses [(13)](#equation-cs-12) and [(14)](#equation-cs-13) to\n", "\n", "- simulate paths of $ y_t, c_t, b_{t+1} $ \n", "- plot these against values of of $ \\bar c, b(s_1), b(s_2) $ found in a corresponding complete markets economy \n", "\n", "\n", "Let’s try this, using the same parameters in both complete and incomplete markets economies" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Random.seed!(42)\n", "N_simul = 150\n", "cp = ConsumptionProblem()\n", "\n", "c̄, b1, b2 = consumption_complete(cp)\n", "debt_complete = [b1, b2]\n", "\n", "c_path, debt_path, y_path, s_path = consumption_incomplete(cp, N_simul=N_simul)\n", "\n", "plt_cons = plot(title = \"Consumption paths\", xlabel = \"Periods\", ylim = [1.4,2.1])\n", "plot!(plt_cons, 1:N_simul, c_path, label = \"incomplete market\", lw = 2)\n", "plot!(plt_cons, 1:N_simul, fill(c̄, N_simul), label = \"complete market\", lw = 2)\n", "plot!(plt_cons, 1:N_simul, y_path, label = \"income\", lw = 2, alpha = 0.6, linestyle = :dash)\n", "plot!(plt_cons, legend = :bottom)\n", "\n", "plt_debt = plot(title = \"Debt paths\", xlabel = \"Periods\")\n", "plot!(plt_debt, 1:N_simul, debt_path, label = \"incomplete market\")\n", "plot!(plt_debt, 1:N_simul, debt_complete[s_path], label = \"complete market\", lw = 2)\n", "plot!(plt_debt, 1:N_simul, y_path, label = \"income\", lw = 2, alpha = 0.6, linestyle = :dash)\n", "plot!(plt_debt, legend = :bottomleft)\n", "\n", "plot(plt_cons, plt_debt, layout = (1,2), size = (800, 400))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the graph on the left, for the same sample path of nonfinancial\n", "income $ y_t $, notice that\n", "\n", "- consumption is constant when there are complete markets, but it takes a random walk in the incomplete markets version of the model \n", "- the consumer’s debt oscillates between two values that are functions\n", " of the Markov state in the complete markets model, while the\n", " consumer’s debt drifts in a “unit root” fashion in the incomplete\n", " markets economy " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Using the Isomorphism\n", "\n", "We can simply relabel variables to acquire tax-smoothing interpretations of our two models" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt_tax = plot(title = \"Tax collection paths\", x_label = \"Periods\", ylim = [1.4,2.1])\n", "plot!(plt_tax, 1:N_simul, c_path, label = \"incomplete market\", lw = 2)\n", "plot!(plt_tax, 1:N_simul, fill(c̄, N_simul), label = \"complete market\", lw = 2)\n", "plot!(plt_tax, 1:N_simul, y_path, label = \"govt expenditures\", alpha = .6, linestyle = :dash,\n", " lw = 2)\n", "\n", "plt_gov = plot(title = \"Government assets paths\", x_label = \"Periods\")\n", "plot!(plt_gov, 1:N_simul, debt_path, label = \"incomplete market\", lw = 2)\n", "plot!(plt_gov, 1:N_simul, debt_complete[s_path], label = \"complete market\", lw = 2)\n", "plot!(plt_gov, 1:N_simul, y_path, label = \"govt expenditures\", alpha = .6, linestyle = :dash,\n", " lw = 2)\n", "hline!(plt_gov, [0], linestyle = :dash, color = :black, lw = 2, label = \"\")\n", "plot(plt_tax, plt_gov, layout = (1,2), size = (800, 400))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Tax Smoothing with Complete Markets\n", "\n", "It is useful to focus on a simple tax-smoothing example with complete markets.\n", "\n", "This example will illustrate how, in a complete markets model like that of Lucas and Stokey [[LS83]](../zreferences.html#lucasstokey1983), the government purchases\n", "insurance from the private sector.\n", "\n", "> - Purchasing insurance protects the government against the need to raise taxes too high or issue too much debt in the high government expenditure event. \n", "\n", "\n", "\n", "We assume that government expenditures move between two values $ G_1 < G_2 $, where Markov state $ 1 $ means “peace” and Markov state $ 2 $ means “war”.\n", "\n", "The government budget constraint in Markov state $ i $ is\n", "\n", "$$\n", "T_i + b_i = G_i + \\sum_j Q_{ij} b_j\n", "$$\n", "\n", "where\n", "\n", "$$\n", "Q_{ij} = \\beta P_{ij}\n", "$$\n", "\n", "is the price of one unit of output next period in state $ j $ when\n", "today’s Markov state is $ i $ and $ b_i $ is the government’s\n", "level of *assets* in Markov state $ i $.\n", "\n", "That is, $ b_i $ is the amount of the one-period loans owned by the government that fall due at time $ t $.\n", "\n", "As above, we’ll assume that the initial Markov state is state $ 1 $.\n", "\n", "In addition, to simplify our example, we’ll set the government’s initial\n", "asset level to $ 0 $, so that $ b_1 =0 $.\n", "\n", "Here’s our code to compute a quantitative example with zero debt in peace time:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hide-output": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P = [0.8 0.2; 0.4 0.6]\n", "Q = [0.768 0.192; 0.384 0.576]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Govt expenditures in peace and war = [1.0, 2.0]\n", "Constant tax collections = 1.3116883116883118\n", "Govt assets in two states = [0.0, 1.6233766233766234]\n", "Now let's check the government's budget constraint in peace and war.\n", "Our assumptions imply that the government always purchases 0 units of the\n", "Arrow peace security.\n", "\n", "Spending on Arrow war security in peace = 0.3116883116883117\n", "Spending on Arrow war security in war = 0.9350649350649349\n", "\n", "\n", "Government tax collections plus asset levels in peace and war\n", "T+b in peace = 1.3116883116883118\n", "T+b in war = 2.9350649350649354\n", "\n", "\n", "Total government spending in peace and war\n", "total govt spending in peace = 1.3116883116883118\n", "total govt spending in war = 2.935064935064935\n", "\n", "\n", "Let's see ex post and ex ante returns on Arrow securities\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ex post returns to purchase of Arrow securities = [1.3020833333333333 5.208333333333333; 2.6041666666666665 1.7361111111111112]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ex ante returns to purchase of Arrow securities = [1.0416666666666667 1.0416666666666667; 1.0416666666666667 1.0416666666666667]\n" ] } ], "source": [ "# Parameters\n", "\n", "β = .96\n", "y = [1.0, 2.0]\n", "b0 = 0.0\n", "P = [0.8 0.2;\n", " 0.4 0.6]\n", "\n", "cp = ConsumptionProblem(β, y, b0, P)\n", "Q = β * P\n", "N_simul = 150\n", "\n", "c̄, b1, b2 = consumption_complete(cp)\n", "debt_complete = [b1, b2]\n", "\n", "println(\"P = $P\")\n", "println(\"Q = $Q\")\n", "println(\"Govt expenditures in peace and war = $y\")\n", "println(\"Constant tax collections = $c̄\")\n", "println(\"Govt assets in two states = $debt_complete\")\n", "\n", "msg = \"\"\"\n", "Now let's check the government's budget constraint in peace and war.\n", "Our assumptions imply that the government always purchases 0 units of the\n", "Arrow peace security.\n", "\"\"\"\n", "println(msg)\n", "\n", "AS1 = Q[1, 2] * b2\n", "println(\"Spending on Arrow war security in peace = $AS1\")\n", "AS2 = Q[2, 2] * b2\n", "println(\"Spending on Arrow war security in war = $AS2\")\n", "\n", "println(\"\\n\")\n", "println(\"Government tax collections plus asset levels in peace and war\")\n", "TB1 = c̄ + b1\n", "println(\"T+b in peace = $TB1\")\n", "TB2 = c̄ + b2\n", "println(\"T+b in war = $TB2\")\n", "\n", "println(\"\\n\")\n", "println(\"Total government spending in peace and war\")\n", "G1= y[1] + AS1\n", "G2 = y[2] + AS2\n", "println(\"total govt spending in peace = $G1\")\n", "println(\"total govt spending in war = $G2\")\n", "\n", "println(\"\\n\")\n", "println(\"Let's see ex post and ex ante returns on Arrow securities\")\n", "\n", "Π = 1 ./ Q # reciprocal(Q)\n", "exret = Π\n", "println(\"Ex post returns to purchase of Arrow securities = $exret\")\n", "exant = Π .* P\n", "println(\"Ex ante returns to purchase of Arrow securities = $exant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explanation\n", "\n", "In this example, the government always purchase $ 0 $ units of the\n", "Arrow security that pays off in peace time (Markov state $ 1 $).\n", "\n", "But it purchases a positive amount of the security that pays off in war\n", "time (Markov state $ 2 $).\n", "\n", "We recommend plugging the quantities computed above into the government\n", "budget constraints in the two Markov states and staring.\n", "\n", "This is an example in which the government purchases *insurance* against\n", "the possibility that war breaks out or continues\n", "\n", "- the insurance does not pay off so long as peace continues \n", "- the insurance pays off when there is war \n", "\n", "\n", "*Exercise:* try changing the Markov transition matrix so that\n", "\n", "$$\n", "P = \\begin{bmatrix}\n", " 1 & 0 \\\\\n", " .2 & .8\n", " \\end{bmatrix}\n", "$$\n", "\n", "Also, start the system in Markov state $ 2 $ (war) with initial\n", "government assets $ - 10 $, so that the government starts the\n", "war in debt and $ b_2 = -10 $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear State Space Version of Complete Markets Model\n", "\n", "Now we’ll use a setting like that in [first lecture on the permanent income model](perm_income.html).\n", "\n", "In that model, there were\n", "\n", "- incomplete markets: the consumer could trade only a single risk-free one-period bond bearing gross one-period risk-free interest rate equal to $ \\beta^{-1} $ \n", "- the consumer’s exogenous nonfinancial income was governed by a linear state space model driven by Gaussian shocks, the kind of model studied in an earlier lecture about [linear state space models](../tools_and_techniques/linear_models.html) \n", "\n", "\n", "We’ll write down a complete markets counterpart of that model.\n", "\n", "So now we’ll suppose that nonfinancial income is governed by the state\n", "space system\n", "\n", "$$\n", "\\begin{aligned}\n", " x_{t+1} & = A x_t + C w_{t+1} \\cr\n", " y_t & = S_y x_t\n", "\\end{aligned}\n", "$$\n", "\n", "where $ x_t $ is an $ n \\times 1 $ vector and $ w_{t+1} \\sim {\\cal N}(0,I) $ is IID over time.\n", "\n", "Again, as a counterpart of the Hall-Barro assumption that the risk-free\n", "gross interest rate is $ \\beta^{-1} $, we assume the scaled prices\n", "of one-period ahead Arrow securities are\n", "\n", "\n", "\n", "$$\n", "p_{t+1}(x_{t+1} \\,|\\, x_t) = \\beta \\phi(x_{t+1} \\,|\\, A x_t, CC') \\tag{15}\n", "$$\n", "\n", "where $ \\phi(\\cdot \\,|\\, \\mu, \\Sigma) $ is a multivariate Gaussian\n", "distribution with mean vector $ \\mu $ and covariance matrix\n", "$ \\Sigma $.\n", "\n", "Let $ b(x_{t+1}) $ be a vector of state-contingent debt due at $ t+1 $\n", "as a function of the $ t+1 $ state $ x_{t+1} $.\n", "\n", "Using the pricing function assumed in [(15)](#equation-cs-14), the value at\n", "$ t $ of $ b(x_{t+1}) $ is\n", "\n", "$$\n", "\\beta \\int b(x_{t+1}) \\phi(x_{t+1} \\,|\\, A x_t, CC') d x_{t+1} = \\beta \\mathbb E_t b_{t+1}\n", "$$\n", "\n", "In the complete markets setting, the consumer faces a sequence of budget\n", "constraints\n", "\n", "$$\n", "c_t + b_t = y_t + \\beta \\mathbb E_t b_{t+1}, t \\geq 0\n", "$$\n", "\n", "We can solve the time $ t $ budget constraint forward to obtain\n", "\n", "$$\n", "b_t = \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j (y_{t+j} - c_{t+j} )\n", "$$\n", "\n", "We assume as before that the consumer cares about the expected value\n", "of\n", "\n", "$$\n", "\\sum_{t=0}^\\infty \\beta^t u(c_t), \\quad 0 < \\beta < 1\n", "$$\n", "\n", "In the incomplete markets version of the model, we assumed that\n", "$ u(c_t) = - (c_t -\\gamma)^2 $, so that the above utility functional\n", "became\n", "\n", "$$\n", "-\\sum_{t=0}^\\infty \\beta^t ( c_t - \\gamma)^2, \\quad 0 < \\beta < 1\n", "$$\n", "\n", "But in the complete markets version, we can assume a more general form\n", "of utility function that satisfies $ u' > 0 $ and $ u'' < 0 $.\n", "\n", "The first-order condition for the consumer’s problem with complete\n", "markets and our assumption about Arrow securities prices is\n", "\n", "$$\n", "u'(c_{t+1}) = u'(c_t) \\quad \\text{for all } t\\geq 0\n", "$$\n", "\n", "which again implies $ c_t = \\bar c $ for some $ \\bar c $.\n", "\n", "So it follows that\n", "\n", "$$\n", "b_t = \\mathbb E_t \\sum_{j=0}^\\infty \\beta^j (y_{t+j} - \\bar c)\n", "$$\n", "\n", "or\n", "\n", "\n", "\n", "$$\n", "b_t = S_y (I - \\beta A)^{-1} x_t - \\frac{1}{1-\\beta} \\bar c \\tag{16}\n", "$$\n", "\n", "where the value of $ \\bar c $ satisfies\n", "\n", "\n", "\n", "$$\n", "\\bar b_0 = S_y (I - \\beta A)^{-1} x_0 - \\frac{1}{1 - \\beta } \\bar c \\tag{17}\n", "$$\n", "\n", "where $ \\bar b_0 $ is an initial level of the consumer’s debt, specified\n", "as a parameter of the problem.\n", "\n", "Thus, in the complete markets version of the consumption-smoothing\n", "model, $ c_t = \\bar c, \\forall t \\geq 0 $ is determined by [(17)](#equation-cs-16)\n", "and the consumer’s debt is a fixed function of\n", "the state $ x_t $ described by [(16)](#equation-cs-15).\n", "\n", "Here’s an example that shows how in this setting the availability of insurance against fluctuating nonfinancial income\n", "allows the consumer completely to smooth consumption across time and across states of the world." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hide-output": false }, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function complete_ss(β, b0, x0, A, C, S_y, T = 12)\n", "\n", " # Create a linear state space for simulation purposes\n", " # This adds \"b\" as a state to the linear state space system\n", " # so that setting the seed places shocks in same place for\n", " # both the complete and incomplete markets economy\n", " # Atilde = vcat(hcat(A, zeros(size(A,1), 1)),\n", " # zeros(1, size(A,2) + 1))\n", " # Ctilde = vcat(C, zeros(1, 1))\n", " # S_ytilde = hcat(S_y, zeros(1, 1))\n", "\n", " lss = LSS(A, C, S_y, mu_0=x0)\n", "\n", " # Add extra state to initial condition\n", " # x0 = hcat(x0, 0)\n", "\n", " # Compute the (I - β*A)^{-1}\n", " rm = inv(I - β * A)\n", "\n", " # Constant level of consumption\n", " cbar = (1 - β) * (S_y * rm * x0 .- b0)\n", " c_hist = ones(T) * cbar[1]\n", "\n", " # Debt\n", " x_hist, y_hist = simulate(lss, T)\n", " b_hist = (S_y * rm * x_hist .- cbar[1] / (1.0 - β))\n", "\n", " return c_hist, vec(b_hist), vec(y_hist), x_hist\n", "end\n", "\n", "N_simul = 150\n", "\n", "# Define parameters\n", "α, ρ1, ρ2 = 10.0, 0.9, 0.0\n", "σ = 1.0\n", "# N_simul = 1\n", "# T = N_simul\n", "A = [1.0 0.0 0.0;\n", " α ρ1 ρ2;\n", " 0.0 1.0 0.0]\n", "C = [0.0, σ, 0.0]\n", "S_y = [1.0 1.0 0.0]\n", "β, b0 = 0.95, -10.0\n", "x0 = [1.0, α / (1 - ρ1), α / (1 - ρ1)]\n", "\n", "# Do simulation for complete markets\n", "out = complete_ss(β, b0, x0, A, C, S_y, 150)\n", "c_hist_com, b_hist_com, y_hist_com, x_hist_com = out\n", "\n", "# Consumption plots\n", "plt_cons = plot(title = \"Cons and income\", xlabel = \"Periods\", ylim = [-5.0, 110])\n", "plot!(plt_cons, 1:N_simul, c_hist_com, label = \"consumption\", lw = 2)\n", "plot!(plt_cons, 1:N_simul, y_hist_com, label = \"income\",\n", " lw = 2, alpha = 0.6, linestyle = :dash)\n", "\n", "# Debt plots\n", "plt_debt = plot(title = \"Debt and income\", xlabel = \"Periods\")\n", "plot!(plt_debt, 1:N_simul, b_hist_com, label = \"debt\", lw = 2)\n", "plot!(plt_debt, 1:N_simul, y_hist_com, label = \"Income\",\n", " lw = 2, alpha = 0.6, linestyle = :dash)\n", "hline!(plt_debt, [0], color = :black, linestyle = :dash, lw = 2, label = \"\")\n", "plot(plt_cons, plt_debt, layout = (1,2), size = (800, 400))\n", "plot!(legend = :bottomleft)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpretation of Graph\n", "\n", "In the above graph, please note that:\n", "\n", "- nonfinancial income fluctuates in a stationary manner \n", "- consumption is completely constant \n", "- the consumer’s debt fluctuates in a stationary manner; in fact, in\n", " this case because nonfinancial income is a first-order\n", " autoregressive process, the consumer’s debt is an exact affine function\n", " (meaning linear plus a constant) of the consumer’s nonfinancial\n", " income " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Incomplete Markets Version\n", "\n", "The incomplete markets version of the model with nonfinancial income being governed by a linear state space system\n", "is described in the first lecture on the [permanent income model](perm_income.html) and the followup\n", "lecture on the [permanent income model](perm_income_cons.html).\n", "\n", "In that version, consumption follows a random walk and the consumer’s debt follows a process with a unit root.\n", "\n", "We leave it to the reader to apply the usual isomorphism to deduce the corresponding implications for a tax-smoothing model like Barro’s [[Bar79]](../zreferences.html#barro1979)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Government Manipulation of Arrow Securities Prices\n", "\n", "In [optimal taxation in an LQ economy](../dynamic_programming_squared/lqramsey.html) and [recursive optimal taxation](../dynamic_programming_squared/opt_tax_recur.html), we study **complete-markets**\n", "models in which the government recognizes that it can manipulate Arrow securities prices.\n", "\n", "In [optimal taxation with incomplete markets](../dynamic_programming_squared/amss.html), we study an **incomplete-markets** model in which the government manipulates asset prices." ] } ], "metadata": { "date": 1591310617.5929081, "download_nb": 1, "download_nb_path": "https://julia.quantecon.org/", "filename": "smoothing.rst", "filename_with_path": "dynamic_programming/smoothing", "kernelspec": { "display_name": "Julia 1.4.2", "language": "julia", "name": "julia-1.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.4.2" }, "title": "Consumption and Tax Smoothing with Complete and Incomplete Markets" }, "nbformat": 4, "nbformat_minor": 2 }