Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models (replication data)

DOI

This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single-agent dynamic discrete-choice models and dynamic games of incomplete information. We propose a full-solution maximum likelihood procedure and a two-step method and use them to estimate an extended version of the capital replacement model of Rust with the original data and in a Monte Carlo study.

Identifier
DOI https://doi.org/10.15456/jae.2022326.0657287299
Metadata Access https://www.da-ra.de/oaip/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:oai.da-ra.de:775535
Provenance
Creator Blevins, Jason R.
Publisher ZBW - Leibniz Informationszentrum Wirtschaft
Publication Year 2016
Rights Creative Commons Attribution 4.0 (CC-BY); Download
OpenAccess true
Contact ZBW - Leibniz Informationszentrum Wirtschaft
Representation
Language English
Resource Type Collection
Discipline Economics