Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

42 Pages Posted: 13 Mar 1999

See all articles by Victor Aguirregabiria

Victor Aguirregabiria

University of Toronto - Department of Economics

Pedro Mira

Centro de Estudios Monetarios y Financieros (CEMFI)

Multiple version iconThere are 2 versions of this paper

Date Written: January 1999

Abstract

This paper proposes a procedure for the estimation of discrete Markov decision models and studies its statistical and computational properties. Our Nested Pseudo-Likelihood method (NPL) is similar to Rust's Nested Fixed Point algorithm (NFXP), but the order of the two nested algorithms is swapped. First, we prove that NPL produces the Maximum Likelihood Estimator under the same conditions as NFXP. Our procedure requires fewer policy iterations at the expense of more likelihood-climbing iterations. We focus on a class of infinite-horizon, partial likelihood problems for which NPL results in large computational gains. Second, based on this algorithm we define a class of consistent and asymptotically equivalent Sequential Policy Iteration (PI) estimators, which encompasses both Hotz-Miller's CCP estimator and the partial Maximum Likelihood estimator. This presents the researcher with a "menu" of sequential estimators reflecting a trade-off between finite-sample precision and computational cost. Using actual and simulated data we compare the relative performance of these estimators. In all our experiments the benefits in terms of precision of using a 2-stage PI estimator instead of 1-stage (i.e., Hotz-Miller) are very significant. More interestingly, the benefits of MLE relative to 2-stage PI are negligible.

JEL Classification: C13, C15, C63

Suggested Citation

Aguirregabiria, Victor and Mira, Pedro, Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models (January 1999). Available at SSRN: https://ssrn.com/abstract=151489 or http://dx.doi.org/10.2139/ssrn.151489

Victor Aguirregabiria (Contact Author)

University of Toronto - Department of Economics ( email )

150 St. George Street
Toronto, Ontario M5S 3G7
Canada
4169784358 (Phone)

HOME PAGE: http://individual.utoronto.ca/vaguirre/

Pedro Mira

Centro de Estudios Monetarios y Financieros (CEMFI) ( email )

Casado del Alisal 5
28014 Madrid
Spain
34 91 429 0551 (Phone)
34 91 429 1056 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
147
Abstract Views
1,043
rank
237,530
PlumX Metrics