A fast and low computational memory algorithm for non-stochastic simulations in heterogeneous agent models

• Published in: Economics letters Vol. 193; p. 109285
• Main Author: Tan, Eugene
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2020; Elsevier Science Ltd
• Subjects: Algorithms; Heterogeneous agent models; Macroeconomics; Markov analysis; Memory; Non-stochastic simulation; Numerical methods; Stochastic models; C6; Non-stochastic simulation; E2; Numerical methods; Heterogeneous agent models
• ISSN: 0165-1765; 1873-7374
• DOI: 10.1016/j.econlet.2020.109285

Heterogeneous agent models in macroeconomics generally require numerical computation of the cross-sectional distribution of agents. The standard textbook approach is to fully approximate the Markov kernel that iterates the distribution forward in time as a Markov transition matrix, which can be costly in terms of computational time and memory when the state space is large. This note provides an alternative algorithm that is simple, requires much less computational memory, and is substantially faster than the standard algorithm. •New algorithm for iterating distributions forward in heterogeneous agent models.•Uses substantially less computational memory and faster than textbook methods.•Gains generally increase when state space of model increases.•Most suitable for interpreted languages (e.g., MATLAB, Python).•General pseudo-code is provided for a broad class of models.