Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/70414
Authors: 
Kasahara, Hiroyuki
Shimotsu, Katsumi
Year of Publication: 
2006
Series/Report no.: 
Research Report, Department of Economics, University of Western Ontario 2006-4
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.
Subjects: 
Edgeworth expansion
k-step bootstrap
maximum pseudo-likelihood estimators
nested fixed point algorithm
Newton-Raphson method
policy iteration
JEL: 
C12
C13
C15
C44
C63
Document Type: 
Working Paper

Files in This Item:
File
Size
428.31 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.