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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen's Economics Department Working Paper ; No. 1063
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Estimation: General
Semiparametric and Nonparametric Methods: General
Statistical Simulation Methods: General
Operations Research; Statistical Decision Theory
Computational Techniques; Simulation Modeling
- Thema
-
Edgeworth expansion
k-step bootstrap
maximum pseudo-likelihood estimators
nested fixed point algorithm
Newton-Raphson method
policy iteration
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Kasahara, Hiroyuki
Shimotsu, Katsumi
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
2006
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Kasahara, Hiroyuki
- Shimotsu, Katsumi
- Queen's University, Department of Economics
Entstanden
- 2006
Ähnliche Objekte (12)
Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models
Inference and Application of Likelihood Based Methods for Hidden Markov Models
Inference and application of likelihood based methods for hidden Markov models
Non-nested models and the likelihood ratio statistic: A comparison of simulation and bootstrap based tests
Improvements in bootstrap inference
Empirical Bayes and likelihood inference
Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response
Applied statistical inference : likelihood and Bayes
Bootstrap and Asymptotic Inference with Multiway Clustering
Bootstrap inference in semiparametric generalized additive models
Dynamic inference in general nested case‐control designs
Likelihood inference in some finite mixture models
Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models
Inference and Application of Likelihood Based Methods for Hidden Markov Models
Inference and application of likelihood based methods for hidden Markov models
Non-nested models and the likelihood ratio statistic: A comparison of simulation and bootstrap based tests
Improvements in bootstrap inference
Empirical Bayes and likelihood inference
Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response
Applied statistical inference : likelihood and Bayes
Bootstrap and Asymptotic Inference with Multiway Clustering
Bootstrap inference in semiparametric generalized additive models
Dynamic inference in general nested case‐control designs
Likelihood inference in some finite mixture models
Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models
Inference and Application of Likelihood Based Methods for Hidden Markov Models
Inference and application of likelihood based methods for hidden Markov models
Non-nested models and the likelihood ratio statistic: A comparison of simulation and bootstrap based tests
Improvements in bootstrap inference
Empirical Bayes and likelihood inference
Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response
Applied statistical inference : likelihood and Bayes
Bootstrap and Asymptotic Inference with Multiway Clustering
Bootstrap inference in semiparametric generalized additive models
Dynamic inference in general nested case‐control designs