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A Research Assistant's Guide to Random Coefficients Discrete Choice Models of Demand

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  • Aviv Nevo

Abstract

The study of differentiated-products markets is a central part of empirical industrial organization. Questions regarding market power, mergers, innovation, and valuation of new brands are addressed using cutting-edge econometric methods and relying on economic theory. Unfortunately, difficulty of use and computational costs have limited the scope of application of recent developments in one of the main methods for estimating demand for differentiated products: random coefficients discrete choice models. As our understanding of these models of demand has increased, both the difficulty and costs have been greatly reduced. This paper carefully discusses the latest innovations in these methods with the hope of (1) increasing the understanding, and therefore the trust, among researchers who never used these methods, and (2) reducing the difficulty of use, and therefore aiding in realizing the full potential of these methods.

Suggested Citation

  • Aviv Nevo, 1998. "A Research Assistant's Guide to Random Coefficients Discrete Choice Models of Demand," NBER Technical Working Papers 0221, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0221
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    1. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
    2. Imbens, G.W. & Lancaster, T., 1991. "Combining Micro and Macro Data in Microeconometric Models," Harvard Institute of Economic Research Working Papers 1578, Harvard - Institute of Economic Research.
    3. Van den Berg, G J & Lindeboom, M & Ridder, G, 1994. "Attrition in Longitudinal Panel Data and the Empirical Analysis of Dynamic Labour Market Behaviour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(4), pages 421-435, Oct.-Dec..
    4. Judith K. Hellerstein & Guido W. Imbens, 1999. "Imposing Moment Restrictions From Auxiliary Data By Weighting," The Review of Economics and Statistics, MIT Press, pages 1-14.
    5. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rebin, 1998. "Combining Panel Data Sets with Attrition and Refreshment Samples," NBER Technical Working Papers 0230, National Bureau of Economic Research, Inc.
    6. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    7. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
    8. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, pages 37-58.
    9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", pages 129-137.
    10. Ridder, Geert, 1992. "An empirical evaluation of some models for non-random attrition in panel data," Structural Change and Economic Dynamics, Elsevier, vol. 3(2), pages 337-355, December.
    11. Imbens, Guido W. & Lancaster, Tony, 1996. "Efficient estimation and stratified sampling," Journal of Econometrics, Elsevier, pages 289-318.
    12. Nijman, Theo & Verbeek, Marno, 1992. "Nonresponse in Panel Data: The Impact on Estimates of a Life Cycle Consumption Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 243-257, July-Sept.
    13. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, pages 329-342.
    14. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, pages 37-58.
    15. Guido W. Imbens & Tony Lancaster, 1994. "Combining Micro and Macro Data in Microeconometric Models," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 655-680.
    16. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, pages 83-114.
    17. Segal, Uzi, 1987. "The Ellsberg Paradox and Risk Aversion: An Anticipated Utility Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(1), pages 175-202, February.
    18. Sonnemans, Joep, 2000. "Decisions and strategies in a sequential search experiment," Journal of Economic Psychology, Elsevier, pages 91-102.
    19. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, pages 239-267.
    20. Becketti, Sean, et al, 1988. "The Panel Study of Income Dynamics after Fourteen Years: An Evaluatio n," Journal of Labor Economics, University of Chicago Press, vol. 6(4), pages 472-492, October.
    21. Abowd, John M & Card, David, 1989. "On the Covariance Structure of Earnings and Hours Changes," Econometrica, Econometric Society, vol. 57(2), pages 411-445, March.
    22. Duncan, Greg J & Hill, Daniel H, 1989. "Assessing the Quality of Household Panel Data: The Case of the Panel Study of Income Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 441-452, October.
    23. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    24. Bound, John, et al, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
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    Cited by:

    1. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    2. Nevo, Aviv, 1997. "Mergers with Differentiated Products: The Case of Ready-to-Eat Cereal," Competition Policy Center, Working Paper Series qt1d53t6ts, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
    3. Mojduszka, Eliza M., 2001. "Integration Of A Product Choice Model And A Latent Variable Model Of Nutrition Information," 2001 Annual meeting, August 5-8, Chicago, IL 20678, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Charles Romeo, 2007. "A Gibbs sampler for mixed logit analysis of differentiated product markets using aggregate data," Computational Economics, Springer;Society for Computational Economics, vol. 29(1), pages 33-68, February.
    5. Khan, Beethika S., 2004. "Consumer Adoption of Online Banking: Does Distance Matter?," Department of Economics, Working Paper Series qt2bt1d76s, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    6. Beethika Khan, 2004. "Consumer Adoption of Online Banking: Does Distance Matter?," Development and Comp Systems 0407002, EconWPA.

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