Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data
CAEPR WORKING PAPER SERIES 2021-001
40 Pages Posted:
Date Written: January 12, 2021
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 a˙ect 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 eÿcient 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
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