Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296324 
Year of Publication: 
2023
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 14 [Issue:] 1 [Year:] 2023 [Pages:] 1-35
Publisher: 
The Econometric Society, New Haven, CT
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.
Subjects: 
Bayesian inference
data combination
heterogeneous agent models
JEL: 
C11
C32
E1
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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