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

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  • Mathias REYNAERT
  • Frank VERBOVEN

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.

Suggested Citation

  • Mathias REYNAERT & Frank VERBOVEN, 2012. "Improving the performance of random coefficients demand models: the role of optimal instruments," Working Papers of Department of Economics, Leuven ces12.07, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
  • Handle: RePEc:ete:ceswps:ces12.07
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    More about this item

    JEL classification:

    • 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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