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

Laura Liu () and Mikkel Plagborg-Moller

CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

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.

Keywords: Bayesian inference; data combination; heterogeneous agent models (search for similar items in EconPapers)
Pages: 40 pages
Date: 2021-01
New Economics Papers: this item is included in nep-dge, nep-isf and nep-ore
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https://caepr.indiana.edu/RePEc/inu/caeprp/CAEPR2021-001.pdf (application/pdf)

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Working Paper: Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data (2022) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:inu:caeprp:2021001

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