IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

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 Chamberlain’s (1987) optimal instruments overcomes many problems that have recently been documented with standard, non-optimal instruments. Optimal instruments reduce small sample bias, but they prove even more powerful in increasing the estimator’s efficiency and stability. We consider a wide variety of data-generating processes and an empirical application to the automobile market. We also consider the gains of other recent methodological advances when combined with optimal instruments.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/pii/S0304407613002649
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 179 (2014)
Issue (Month): 1 ()
Pages: 83-98

as
in new window

Handle: RePEc:eee:econom:v:179:y:2014:i:1:p:83-98
DOI: 10.1016/j.jeconom.2013.12.001
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. 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.
  2. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-90, July.
  3. Aviv Nevo, 2003. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Microeconomics 0303006, EconWPA.
  4. Michelle Sovinsky Goeree, 2008. "Limited Information and Advertising in the U.S. Personal Computer Industry," Econometrica, Econometric Society, vol. 76(5), pages 1017-1074, 09.
  5. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
  6. Patrick Bajari & Jeremy Fox & Kyoo il Kim & Stephen P. Ryan, 2009. "The Random Coefficients Logit Model Is Identified," NBER Working Papers 14934, National Bureau of Economic Research, Inc.
  7. Steven T. Berry & Amit Gandhi & Philip Haile, 2011. "Connected Substitutes and Invertibility of Demand," NBER Working Papers 17193, National Bureau of Economic Research, Inc.
  8. Frank Verboven, 1996. "International Price Discrimination in the European Car Market," RAND Journal of Economics, The RAND Corporation, vol. 27(2), pages 240-268, Summer.
  9. 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.
  10. Newey, Whitney K, 1990. "Efficient Instrumental Variables Estimation of Nonlinear Models," Econometrica, Econometric Society, vol. 58(4), pages 809-37, July.
  11. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  12. 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.
  13. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
  14. Che‐Lin Su & Kenneth L. Judd, 2012. "Constrained Optimization Approaches to Estimation of Structural Models," Econometrica, Econometric Society, vol. 80(5), pages 2213-2230, 09.
  15. Pinelopi K. Goldberg & Rebecca Hellerstein, 2007. "A Structural Approach to Identifying the Sources of Local-Currency Price Stability," NBER Working Papers 13183, National Bureau of Economic Research, Inc.
  16. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
  17. 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.
  18. Michelle Sovinsky Goeree, 2005. "Advertising in the US Personal Computer Industry," Industrial Organization 0503002, EconWPA.
  19. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, December.
  20. Crawford, Gregory S., 2012. "Endogenous product choice: A progress report," International Journal of Industrial Organization, Elsevier, vol. 30(3), pages 315-320.
  21. 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.
  22. 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.
  23. Amemiya, Takeshi, 1977. "The Maximum Likelihood and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model," Econometrica, Econometric Society, vol. 45(4), pages 955-68, May.
  24. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
  25. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:179:y:2014:i:1:p:83-98. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.