Title

Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data

Author(s)

Laura Liu Laura Liu (Indiana University)

Mikkel Plagborg-Møller Mikkel Plagborg-Møller (Princeton University)

Abstract

We develop a generally applicable full-information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross sections of micro data. To handle unobserved aggregate state variables that affect cross-sectional distributions, we compute a numerically unbiased estimate of the model-implied likelihood function. Employing the likelihood estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and valid Bayesian inference. Evaluation of the micro part of the likelihood lends itself naturally to parallel computing. Numerical illustrations in models with heterogeneous households or firms demonstrate that the proposed full-information method substantially sharpens inference relative to using only macro data, and for some parameters micro data is essential for identification.

Creation Date

2022-06

Section URL ID

Paper Number

2022-21

URL

https://scholar.princeton.edu/sites/default/files/het_agents.pdf

File Function

Jel

C11, C32, E1

Keyword(s)

Bayesian inference, data combination, heterogeneous agent models

Suppress

false

Series

13