Full-information estimation of heterogeneous agent models using macro and micro data

• 成果类型: Article
• 署名作者: Liu, Laura; Plagborg-Moller, Mikkel
• 署名单位: Indiana University System; Indiana University Bloomington; Princeton University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1810
• 发表日期: 2023
• 页码: 1-35
• 关键词: Bayesian inference; data combination; heterogeneous agent models; C11; C32; e1

摘要

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.