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

Hiroyuki Kasahara and Katsumi Shimotsu

No 20064, University of Western Ontario, Departmental Research Report Series from University of Western Ontario, Department of Economics

Abstract: 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.

Keywords: k-step bootstrap; maximum pseudo-likelihood estimators; nested fixed point algorithm; Newton-Raphson method; policy iteration. (search for similar items in EconPapers)
JEL-codes: C12 C13 C15 C44 C63 (search for similar items in EconPapers)
Date: 2006
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)

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Working Paper: Nested Pseudo-likelihood Estimation And Bootstrap-based Inference For Structural Discrete Markov Decision Models (2006)
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