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

Hiroyuki Kasahara and Katsumi Shimotsu

No 273539, Queen's Economics Department Working Papers from Queen's University - 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: Financial Economics; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 58
Date: 2006-02
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Related works:
Working Paper: Nested Pseudo-likelihood Estimation And Bootstrap-based Inference For Structural Discrete Markov Decision Models (2006)
Working Paper: Nested Pseudo-likelihood Estimation and Bootstrap-based Inference for Structural Discrete Markov Decision Models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:quedwp:273539

DOI: 10.22004/ag.econ.273539

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