Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

Jason R. Blevins
Journal of Applied Econometrics 31 (2016), 773–804.

Availability:

• Published version (DOI: 10.1002/jae.2470)
• Preprint version (February 19, 2015).
• Ohio State University Working Paper 11–01, May 2, 2011: IDEAS | EconPapers | Ohio State
• JAE Data Archive

Abstract. 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.

Keywords: dynamic discrete choice, latent state variables, serial correlation, dynamic selection, sequential Monte Carlo methods, particle filtering.

JEL Classification: C13, C15, C35.

BibTeX Record:

@Article{blevins16sequential,
  author       = {Jason R. Blevins},
  title        = {Sequential {Monte Carlo} Methods for Estimating Dynamic
                  Microeconomic Models},
  year         = {2016},
  journal      = {Journal of Applied Econometrics},
  volume       = 31,
  pages        = {773--804}
}