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  • 标题:Semiparametric Estimation of Markov Decision Processeswith Continuous State Space
  • 本地全文:下载
  • 作者:Sorawoot Srisuma ; Oliver Linton
  • 期刊名称:Distributional Analysis Publications
  • 印刷版ISSN:1352-2469
  • 出版年度:2010
  • 卷号:2010
  • 出版社:Suntory Toyota International Centres for Economics and Related Disciplines
  • 摘要:We propose a general two-step estimation method for the structural parameters of popular semiparametric Markovian discrete choice models that include a class of Markovian Games and allow for continuous observable state space. The estimation procedure is simple as it directly generalizes the computationally attractive methodology of Pesendorfer and Schmidt-Dengler (2008) that assumed finite observable states. This extension is non-trivial as the value functions, to be estimated nonparametrically in the first stage, are defined recursively in a non-linear functional equation. Utilizing structural assumptions, we show how to consistently estimate the infinite dimensional parameters as the solution to some type II integral equations, the solving of which is a well-posed problem. We provide sufficient set of primitives to obtain root-T consistent estimators for the finite dimensional structural parameters and the distribution theory for the value functions in a time series framework
  • 关键词:Discrete Markov Decision Models; Kernel Smoothing; Markovian ;Games; Semi-parametric Estimation; Well-Posed Inverse Problem
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