Semiparametric estimation of Markov decision processes with continuous state space
Sorawoot Srisuma and
Oliver Linton
Journal of Econometrics, 2012, vol. 166, issue 2, 320-341
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.
Keywords: Discrete Markov decision models; Kernel smoothing semiparametric estimation; Well-posed inverse problem (search for similar items in EconPapers)
JEL-codes: C13 C14 C51 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (21)
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Related works:
Working Paper: Semiparametric Estimation of Markov Decision Processeswith Continuous State Space (2010) 
Working Paper: Semiparametric estimation of Markov decision processeswith continuous state space (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:166:y:2012:i:2:p:320-341
DOI: 10.1016/j.jeconom.2011.10.003
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