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

  • REYNAERT, Mathias
  • VERBOVEN, Frank

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 Chamberlains (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 estimators 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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Paper provided by University of Antwerp, Faculty of Applied Economics in its series Working Papers with number 2012011.

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Length: 31 pages
Date of creation: Jun 2012
Date of revision:
Handle: RePEc:ant:wpaper:2012011
Contact details of provider: Postal: Prinsstraat 13, B-2000 Antwerpen
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  1. Aviv Nevo, 2003. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Microeconomics 0303006, EconWPA.
  2. James Levinsohn & Steven Berry & Ariel Pakes, 1999. "Voluntary Export Restraints on Automobiles: Evaluating a Trade Policy," American Economic Review, American Economic Association, vol. 89(3), pages 400-430, June.
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  4. Newey, Whitney K, 1990. "Efficient Instrumental Variables Estimation of Nonlinear Models," Econometrica, Econometric Society, vol. 58(4), pages 809-37, July.
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  9. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
  10. Che-Lin Su & Kenneth L. Judd, 2008. "Constrainted Optimization Approaches to Estimation of Structural Models," Discussion Papers 1460, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  11. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
  12. Kenneth L. Judd & Ben Skrainka, 2011. "High performance quadrature rules: how numerical integration affects a popular model of product differentiation," CeMMAP working papers CWP03/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  13. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
  14. Michelle Sovinsky Goeree, 2005. "Advertising in the US Personal Computer Industry," Industrial Organization 0503002, EconWPA.
  15. Fox, Jeremy T. & Kim, Kyoo il & Ryan, Stephen P. & Bajari, Patrick, 2012. "The random coefficients logit model is identified," Journal of Econometrics, Elsevier, vol. 166(2), pages 204-212.
  16. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
  17. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
  18. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
  19. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-90, July.
  20. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  21. Jean-Pierre H. Dubé & Jeremy T. Fox & Che-Lin Su, 2009. "Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation," NBER Working Papers 14991, National Bureau of Economic Research, Inc.
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  23. Aviv Nevo, 2000. "A Practitioner's Guide to Estimation of Random-Coefficients Logit Models of Demand," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 9(4), pages 513-548, December.
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  25. Jean‐Pierre Dubé & Jeremy T. Fox & Che‐Lin Su, 2012. "Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation," Econometrica, Econometric Society, vol. 80(5), pages 2231-2267, 09.
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