Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation
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Date: 11-10-2009
Start Time:
1:00pm
End Time: 2:00pm
Speaker: Che-Lin Su, University of Chicago Booth School of Business
Location: Uris 333
ABSTRACT
The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to compute the BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a constrained optimization problem, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking demand models where Bellman's equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization.
This is joint work with Jean-Pierre Dube (Chicago Booth) and Jeremy T. Fox (Department of Economics, U of Chicago).
BIO
Che-Lin Su is an Assistant Professor of Operations Management at the University of Chicago Booth School of Business. He is interested in developing and applying computational methods to study business applications that arise in operations, economics, accounting, and quantitative marketing. His current research topics include empirical estimation of economics models, incentive problems with applications to executive compensation design and nonlinear pricing, and dynamic competition between firms. Before joining the Chicago Booth faculty in 2008, he was a postdoctoral research fellow at the Center for Mathematical Studies in Economics and Management Science at the Kellogg School of Management at Northwestern University and the NBER in Cambridge, Massachusetts. He received his Ph.D in Management Science and Engineering from Stanford University in 2005.