Semiparametric estimation of Markov decision processes with continuous state space

Sorawoot Srisuma, Oliver Linton

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

We propose a general two-step estimator for a popular Markov discrete choice model that includes a class of Markovian games with continuous observable state space. Our estimation procedure generalizes the computationally attractive methodology of Pesendorfer and Schmidt-Dengler (2008) that assumed finite observable states. This extension is non-trivial as the policy value functions are solutions to some type II integral equations. We show that the inverse problem is well-posed. We provide a set of primitive conditions to ensure root-T consistent estimation for the finite dimensional structural parameters and the distribution theory for the value functions in a time series framework.

Original languageEnglish
Pages (from-to)320-341
Number of pages22
JournalJournal of Econometrics
Volume166
Issue number2
DOIs
Publication statusPublished - Feb 2012
Externally publishedYes

Keywords

  • Discrete Markov decision models
  • Kernel smoothing semiparametric estimation
  • Well-posed inverse problem

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