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

CAEPR WORKING PAPER SERIES 2021-001

40 Pages Posted: 9 Mar 2021

See all articles by Laura Liu

Laura Liu

Indiana University Bloomington - Department of Economics

Mikkel Plagborg-Møller

Harvard University - Department of Economics

Date Written: January 12, 2021

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 it-self 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.

Keywords: Bayesian Inference, Data Combination, Heterogeneous Agent Models

JEL Classification: C11, C32, E1

Suggested Citation

Liu, Laura and Plagborg-Møller, Mikkel, Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data (January 12, 2021). CAEPR WORKING PAPER SERIES 2021-001, Available at SSRN: https://ssrn.com/abstract=3765532 or http://dx.doi.org/10.2139/ssrn.3765532

Laura Liu (Contact Author)

Indiana University Bloomington - Department of Economics ( email )

Wylie Hall
Bloomington, IN 47405-6620
United States

HOME PAGE: http://https://laurayuliu.com/

Mikkel Plagborg-Møller

Harvard University - Department of Economics ( email )

Littauer Center
Cambridge, MA 02138
United States

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