Arbeitspapier

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

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.

Language
Englisch

Bibliographic citation
Series: Queen's Economics Department Working Paper ; No. 1063

Classification
Wirtschaft
Hypothesis Testing: General
Estimation: General
Semiparametric and Nonparametric Methods: General
Statistical Simulation Methods: General
Operations Research; Statistical Decision Theory
Computational Techniques; Simulation Modeling
Subject
Edgeworth expansion
k-step bootstrap
maximum pseudo-likelihood estimators
nested fixed point algorithm
Newton-Raphson method
policy iteration

Event
Geistige Schöpfung
(who)
Kasahara, Hiroyuki
Shimotsu, Katsumi
Event
Veröffentlichung
(who)
Queen's University, Department of Economics
(where)
Kingston (Ontario)
(when)
2006

Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Kasahara, Hiroyuki
  • Shimotsu, Katsumi
  • Queen's University, Department of Economics

Time of origin

  • 2006

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