Nested Pseudo-likelihood Estimation And Bootstrap-based Inference For Structural Discrete Markov Decision Models
Hiroyuki Kasahara () and
No 1063, Working Paper from Economics Department, 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.
Keywords: Edgeworth expansion; 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 C14 C15 C44 C63 (search for similar items in EconPapers)
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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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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1063
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