Improving the performance of random coefficients demand models: The role of optimal instruments

Mathias Reynaert () and Frank Verboven

Working Papers from University of Antwerp, Faculty of Applied Economics

Abstract: We shed new light on the performance of Berry, Levinsohn and Pakes? (1995) GMM estimator of the aggregate random coe¢ cient logit model. Based on an extensive Monte Carlo study, we show that the use of Chamberlain?s (1987) optimal instruments overcomes most of the problems that have recently been documented with standard, non-optimal instruments. Optimal instruments reduce small sample bias, but prove even more powerful in increasing the estimator?s e¢ ciency and stability. Other recent methodological advances (MPEC, polynomial-based integration of the market shares) greatly improve computational speed, but they are only successful in terms of bias and e¢ ciency when combined with optimal instruments.

Date: 2012-06
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Related works:
Working Paper: Improving the Performance of Random Coefficients Demand Models: the Role of Optimal Instruments (2012)
Working Paper: Improving the performance of random coefficients demand models: the role of optimal instruments (2012)
Working Paper: Improving the performance of random coefficients demand models: the role of optimal instruments (2012)
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