Estimation of Heterogeneous Agent Models: A Likelihood Approach

Juan Carlos Parra‐Alvarez, Olaf Posch and Mu‐Chun Wang

Oxford Bulletin of Economics and Statistics, 2023, vol. 85, issue 2, 304-330

Abstract: 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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/obes.12531

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:obuest:v:85:y:2023:i:2:p:304-330

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0305-9049

Access Statistics for this article

Oxford Bulletin of Economics and Statistics is currently edited by Christopher Adam, Anindya Banerjee, Christopher Bowdler, David Hendry, Adriaan Kalwij, John Knight and Jonathan Temple

More articles in Oxford Bulletin of Economics and Statistics from Department of Economics, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().