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Nested Pseudo-likelihood Estimation and Bootstrap-based Inference for Structural Discrete Markov Decision Models

  • Hiroyuki Kasahara

    ()

    (University of Western Ontario)

  • Katsumi Shimotsu

    ()

    (Queen's University)

This paper analyzes the higher-order properties of nested pseudo-likelihood (NPL) estimators and their practical implementation for parametric discrete Markov decision models in which the probability distribution is defined as a fixed point. We propose a new NPL estimator that can achieve quadratic convergence without fully solving the fixed point problem in every iteration. We then extend the NPL estimators to develop one-step NPL bootstrap procedures for discrete Markov decision models and provide some Monte Carlo evidence based on a machine replacement model of Rust (1987). The proposed one-step bootstrap test statistics and confidence intervals improve upon the first order asymptotics even with a relatively small number of iterations. Improvements are particularly noticeable when analyzing the dynamic impacts of counterfactual policies.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1063.pdf
File Function: First version 2006
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1063.

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Length: 56 pages
Date of creation: Feb 2006
Date of revision:
Handle: RePEc:qed:wpaper:1063
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