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Keywords:Computational methods 

Working Paper
Computing Equilibria of Stochastic Heterogeneous Agent Models Using Decision Rule Histories

This paper introduces a general method for computing equilibria with heterogeneous agents and aggregate shocks that is particularly suitable for economies with private information. Instead of the cross-sectional distribution of agents across individual states, the method uses as a state variable a vector of spline coefficients describing a long history of past individual decision rules. Applying the computational method to a Mirrlees RBC economy with known analytical solution recovers the solution perfectly well. This test provides considerable confidence on the accuracy of the method.
Working Paper Series , Paper WP 2020-05

Working Paper
A Generalized Time Iteration Method for Solving Dynamic Optimization Problems with Occasionally Binding Constraints

We study a generalized version of Coleman (1990)’s time iteration method (GTI) for solving dynamic optimization problems. Our benchmark framework is an irreversible investment model with labor-leisure choice. The GTI algorithm is simple to implement and provides advantages in terms of speed relative to Howard (1960)’s improvement algorithm. A second application on a heterogeneous-agents incomplete-markets model further explores the performance of GTI.
Globalization Institute Working Papers , Paper 396

Working Paper
Computing Equilibria of Stochastic Heterogeneous Agent Models Using Decision Rule Histories

This paper introduces a general method for computing equilibria with heterogeneous agents and aggregate shocks that is particularly suitable for economies with private information. Instead of the cross-sectional distribution of agents across individual states, the method uses as a state variable a vector of spline coefficients describing a long history of past individual decision rules. Applying the computational method to a Mirrlees RBC economy with known analytical solution recovers the solution perfectly well. This test provides considerable confidence on the accuracy of the method.
Working Paper Series , Paper WP-2020-05

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