Divide and Conquer: A New Approach to Dynamic Discrete Choice with Serial Correlation

19 Pages Posted: 25 Dec 2013

See all articles by Gregor Reich

Gregor Reich

Norwegian School of Economics (NHH) - Department of Strategy and Management

Date Written: December 24, 2013

Abstract

In this paper, we develop a method to efficiently estimate dynamic discrete choice models with AR(n) type serial correlation of the errors. First, to approximate the expected value function of the underlying dynamic problem, we use Gaussian quadrature, interpolation over an adaptively refined grid, and solve a potentially large non-linear system of equations. Second, to evaluate the likelihood function, we decompose the integral over the unobserved state variables in the likelihood function into a series of lower dimensional integrals, and successively approximate them using Gaussian quadrature rules. Finally, we solve the maximum likelihood problem using a nested fixed point algorithm. We then apply this method to obtain point estimates of the parameters of the bus engine replacement model of Rust [Econometrica, 55 (5): 999–1033, (1987)]: First, we verify the algorithm's ability to recover the parameters of an artificial data set, and second, we estimate the model using the original data, finding significant serial correlation for some sub-samples.

Keywords: Dynamic discrete choice models, Numerical dynamic programming, Gaussian quadrature, Adaptive grids, Backward iteration

JEL Classification: C25, C63

Suggested Citation

Reich, Gregor, Divide and Conquer: A New Approach to Dynamic Discrete Choice with Serial Correlation (December 24, 2013). Available at SSRN: https://ssrn.com/abstract=2371592 or http://dx.doi.org/10.2139/ssrn.2371592

Gregor Reich (Contact Author)

Norwegian School of Economics (NHH) - Department of Strategy and Management ( email )

Breiviksveien 40
N-5045 Bergen
Norway

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

Paper statistics

Downloads
73
Abstract Views
488
rank
385,175
PlumX Metrics