# Huggett (1997): Steady States and Transition Paths in Heterogeneous Agent Models¶

## The Model¶

We use the seminal work Huggett (1997) to illustrate how the toolbox can be used to solve steady states and transition paths of a heterogeneous-agent model. The example also demonstrates how to conduct non-stochastic simulations using the toolbox, by keeping track of the distribution function over a refined grid of individual state variables.

Though the toolbox is not designed for solving the equilibrium of this type of model directly, since the decision problem is characterized by an equation system (the Euler equation

$u'(c_t) = \beta \mathbb{E}_t[(1+r_{t+1})u'(c_{t+1})] + \lambda_t,$

where $$\lambda_t$$ is the Lagrange multiplier on the borrowing constraint, and the complementary-slackness condition, $$\lambda_t k_{t+1}=0$$) with state transition functions, it readily fits in the toolbox’s general framework. One just needs an extra fixed-point loop to update the aggregate equilibrium object, which can be coded in MATLAB. For the one-sector model studied by Huggett (1997), the steady state aggregate equilibrium object is the aggregate capital stock; the transition path aggregate equilibrium object is the time sequence of the aggregate capital stock.

We directly define the equilibrium, which covers all the ingredients we need for computing the model. For the full description of the model, see Huggett (1997) .

A sequential equilibrium is a time sequence of (1) policy functions $$c_{t}(k,e)$$, $$\lambda_t(k,e)$$, $$k'_t(k,e)$$; (2) measures over individual states $$\phi_t$$; (3) aggregate prices and quantities $$w_t, r_t, K_t$$, s.t.

1. $$c_t(k,e), \lambda_t(k,e), k_t'(k,e)$$ satisfy individuals’ optimality conditions. That is, they solve

$\begin{split}c_t(k,e)^{-\sigma}=\beta(1+r_{t+1})E[c_{t+1}(k_t'(k,e),e')^{-\sigma} | e] + \lambda_t(k,e), \\ k_t'(k,e)\lambda_t(k,e)=0, \quad \lambda_t(k,e)\geq0, \quad k_t'(k,e)\geq0, \\ c_t(k,e)+k_t'(k,e)=k(1+r_t)+w_te.\end{split}$
1. Prices are competitively determined and markets clearing:

$\begin{split}r_t = \alpha K_t^{\alpha-1}-\delta \\ w_t = (1-\alpha) K_t^{\alpha} \\ K_t = \int k \ \phi_t(dk,de)\end{split}$
1. $$\phi_t$$ are consistent with the transitions implied by policy functions and exogenous shocks.

A steady-state equilibrium is a sequential equilibrium with time-invariant equilibrium objects.

Notice we have transformed the individual’s optimization problem into first order conditions and complementarity slackness conditions, which enable us to solve the decision problem with the toolbox.

## The gmod File and MATLAB File¶

huggett1997.gmod

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 % Toolbox options INTERP_ORDER = 4; ExtrapOrder = 4; SIMU_RESOLVE = 0; SIMU_INTERP = 1; SaveFreq = inf; PrintFreq = 100; TolEq = 1e-6; % Parameters parameters beta sigma kMin r w; beta = 0.96; % discount factor sigma = 1.5; % CRRA coefficient alpha = 0.36; % capital share in production delta = 0.1; % depreciation rate % States var_state k; kPts = 100; kMin = 0; kMax = 20; kShift = 1e-3; k = exp(linspace(log(kMin+1e-3),log(kMax+1e-3),kPts)) - 1e-3; % Shock process in Huggett (1997) var_shock e; e = [0.8, 1.2]; shock_num = 2; shock_trans = [0.5,0.5;0.5,0.5]; % Representative-agent steady state kSs = ( (1/beta+delta-1) / alpha )^(1/(alpha-1)); % Initial prices r = alpha*kSs^(alpha-1) - delta; w = (1-alpha)*kSs^alpha; % State transition functions var_interp Evp_interp; initial Evp_interp (k.*(1+r)+e.*w).^(-sigma); % Update Evp_interp = shock_trans*vp; % Endogenous variables var_policy k_next lambda; inbound k_next kMin k.*(1+r)+e.*w; inbound lambda 0 1.0; % Other variables var_aux c vp; % Used in simulation var_output c k_next; var_others kSs alpha delta output_interp_t; TASK = 'ss'; % Default task, need overwritten output_interp_t = {}; % Default transition path; pre_iter; % The pre_iter block will be called at the beginning of every policy iteration switch TASK case 'ss' case 'transition' t = T - GNDSGE_Iter + 1; % Convert forward to backward r = r_t(t); w = w_t(t); end end; model; budget = k*(1+r) + e*w; c = budget - k_next; up = c^(-sigma); [Evp_future] = GDSGE_INTERP_VEC(shock,k_next); euler_residual = -1 + beta*Evp_future/up + lambda; vp = up*(1+r); % Envelope theorem equations; euler_residual; lambda*(k_next-kMin); end; end; post_iter; % The post_iter block will be called at the end of every policy iteration switch TASK case 'transition' % The following code constructs function approximation for var_output % and stores in IterRslt.output_interp OUTPUT_CONSTRUCT_CODE; % Store the period-t equilbrium object output_interp_t{t} = IterRslt.output_interp; end end; simulate; num_periods = 1; num_samples = 10000; initial k kSs; % A place holder initial shock 1; % A place holder var_simu c; k' = k_next; end; 

The MATLAB file that calls the toolbox codes and manually update equilibrium objects main.m

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 `%% Solve a WarmUp problem IterRslt = iter_huggett1997; %% A fixed-point loop to solve the initial steady state tolEq = 1e-5; metric = inf; iter = 0; UPDATE_SPEED = 0.01; K = IterRslt.var_others.kSs; alpha = IterRslt.var_others.alpha; delta = IterRslt.var_others.delta; % Non-stochastic simulation, prepare distribution grid kFinePts = 1000; shockPts = IterRslt.shock_num; kFine = linspace(min(IterRslt.var_state.k),max(IterRslt.var_state.k),kFinePts)'; kFineRight = [kFine(2:end);inf]; [kFineGrid,shockGrid] = ndgrid(kFine,1:shockPts); % Parameters to simulate only one step simuOptions.num_periods = 1; simuOptions.num_samples = numel(kFineGrid); simuOptions.init.k = kFineGrid(:); simuOptions.init.shock = shockGrid(:); while metric > tolEq % Solve at prices implied by current K options = struct; options.TASK = 'ss'; options.r = alpha*K^(alpha-1) - delta; options.w = (1-alpha)*K^alpha; options.WarmUp = IterRslt; IterRslt = iter_huggett1997(options); % Non-stochastic simulation. Simulate one-step to get the state transition % functions over kFine SimuRslt = simulate_huggett1997(IterRslt,simuOptions); % Construct the Markov transition implied by policy functions kp = SimuRslt.k(:,2); [~,kpCell] = histc(kp, [kFine;inf]); leftWeights = (kFineRight(kpCell)-kp) ./ (kFineRight(kpCell)-kFine(kpCell)); leftWeights(kpCell>=kFinePts) = 1; rowVec = [1:shockPts*kFinePts]'; transToKp = sparse(rowVec,kpCell,leftWeights,shockPts*kFinePts,kFinePts) ... + sparse(rowVec(kpCellkBar,1); dist0(2:kBarIndex,:) = 0.8 / numel(dist0(2:kBarIndex,:)); dist0(kBarIndex+1:end,:) = 0; while metric > tolEq % Backward loop options = struct; options.TASK = 'transition'; options.PrintFreq = inf; options.MaxIter = T; options.T = T; options.TolEq = 0; % Do not check TolEq options.r_t = alpha*K_t.^(alpha-1) - delta; options.w_t = (1-alpha)*K_t.^alpha; options.WarmUp = IterRslt; % Start from steady state options.WarmUp.Iter = 0; % Start with iter 0; IterRslt_t = iter_huggett1997(options); % Forward simulation dist = dist0; for t=1:1:T K_t_new(t) = sum(reshape(dist.*reshape(kFine,[kFinePts,1]), [], 1)); % Simulate using period-t policies IterRslt.output_interp = IterRslt_t.var_others.output_interp_t{t}; SimuRslt_t = simulate_huggett1997(IterRslt,simuOptions); % Construct the Markov transition implied by policy functions kp = SimuRslt_t.k(:,2); [~,kpCell] = histc(kp, [kFine;inf]); leftWeights = (kFineRight(kpCell)-kp) ./ (kFineRight(kpCell)-kFine(kpCell)); leftWeights(kpCell>=kFinePts) = 1; rowVec = [1:shockPts*kFinePts]'; transToKp = sparse(rowVec,kpCell,leftWeights,shockPts*kFinePts,kFinePts) ... + sparse(rowVec(kpCell

## Output¶

The code produces the stationary distribution the transition path starting from an equal wealth distribution (see the MATLAB file for how the initial distribution is constructed) and the policy functions at the steady state. 