Improving the performance of random coefficients demand models: the role of optimal instruments
AbstractWe 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.
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Bibliographic InfoPaper provided by Katholieke Universiteit Leuven, Centrum voor Economische Studiën in its series Center for Economic Studies - Discussion papers with number ces12.07.
Date of creation: Jun 2012
Date of revision:
Other versions of this item:
- Reynaert, Mathias & Verboven, Frank, 2014. "Improving the performance of random coefficients demand models: The role of optimal instruments," Journal of Econometrics, Elsevier, vol. 179(1), pages 83-98.
- Reynaert, Mathias & Verboven, Frank, 2012. "Improving the Performance of Random Coefficients Demand Models: the Role of Optimal Instruments," CEPR Discussion Papers 9026, C.E.P.R. Discussion Papers.
- REYNAERT, Mathias & VERBOVEN, Frank, 2012. "Improving the performance of random coefficients demand models: The role of optimal instruments," Working Papers 2012011, University of Antwerp, Faculty of Applied Economics.
- C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
- L00 - Industrial Organization - - General - - - General
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