Improving the Performance of Random Coefficients Demand Models: the Role of Optimal Instruments

Frank Verboven and Mathias Reynaert

No 9026, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We shed new light on the performance of Berry, Levinsohn and Pakes' (1995) GMM estimator of the aggregate random coefficient 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 efficiency 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 efficiency when combined with optimal instruments.

Keywords: Optimal instruments; Random coefficients demand model (search for similar items in EconPapers)
JEL-codes: C36 L00 (search for similar items in EconPapers)
Date: 2012-06
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Citations: View citations in EconPapers (7)

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Journal Article: Improving the performance of random coefficients demand models: The role of optimal instruments (2014)
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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