Semiparametric Estimation of Markov Decision Processeswith Continuous State Space
Oliver Linton () and
STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE
We propose a general two-step estimation method for the structural parameters ofpopular semiparametric Markovian discrete choice models that include a class ofMarkovian Games andallow for continuous observable state space. The estimation procedure is simpleas it directly generalizes the computationally attractive methodology of Pesendorferand Schmidt-Dengler (2008) that assumed finite observable states. This extensionis non-trivial as the value functions, to be estimated nonparametrically in the firststage, are defined recursively in a non-linear functional equation. Utilizingstructural assumptions, we show how to consistently estimate the infinitedimensional parameters as the solution to some type II integral equations, thesolving of which is a well-posed problem. We provide sufficient set of primitives toobtain root-T consistent estimators for the finite dimensional structural parametersand the distribution theory for the value functions in a time series framework.
Keywords: Discrete Markov Decision Models; Kernel Smoothing; Markovian Games; Semi-parametric Estimation; Well-Posed Inverse Problem.D (search for similar items in EconPapers)
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Journal Article: Semiparametric estimation of Markov decision processes with continuous state space (2012)
Working Paper: Semiparametric estimation of Markov decision processeswith continuous state space (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:550
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