Estimation of Heterogeneous Agent Models

A Likelihood Approach

Bibliographic Data

ID21512671
AuthorsJuan Carlos Parra‐Alvarez (0000-0001-9076-9021, Department of Economics and Business Economics Aarhus University Fuglesangs Allé 4 8210 Aarhus V Denmark), Olaf Posch (CREATES Center for Research in Econometric Analysis of Time Series Aarhus V 8210 Denmark), Mu‐Chun Wang (0000-0002-4605-0658, Deutsche Bundesbank Wilhelm‐Epstein‐Strasse 14 60431 Frankfurt am Main Germany)
Year2023
Volume85
Issue2
Pages304-330
Publication date2023-04-01
Open AccessYes
TypeARTICLE
VenueOxford Bulletin of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0305-9049 • E-ISSN: 1468-0084
PublisherWiley (PUBLISHER • GB)
DOI10.1111/obes.12531
OpenAlexW3147841542
LanguageEN
References cited54

Using a Bewley‐Hugget‐Aiyagari model we show how to use the Fokker‐Planck equation for likelihood inference in heterogeneous agent (HA) models. We study the finite sample properties of the maximum likelihood estimator (MLE) in Monte Carlo experiments using cross‐sectional data on wealth and income. We use the Kullback–Leibler divergence to investigate identification problems that may affect inference. Unrestricted MLE leads to considerable biases of some parameters. Calibrating weakly identified parameters is shown to be useful to pin down the remaining structural parameters. We illustrate our approach by estimating the model for the US economy using the Survey of Consumer Finances

Econometrics · Estimator · Inference · Kullback–Leibler divergence · Maximum likelihood · Monte Carlo method · Physics · Statistics · Complex Systems and Time Series Analysis · Computer Science · Economic theories and models · Mathematics · Monetary Policy and Economic Impact · Applied Mathematics · Artificial Intelligence

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Open DOIOpen Access