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Estimating Multiple-Discrete Choice Models: An Application to Computeri-zzation Returns

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  • Igal Hendel

Abstract

This paper develops a multiple-discrete choice model for the analysis of demand of differentiated products. Users maximize profits by choosing the number of units of each brand they purchase. Multiple-unit as well as multiple-brand purchases are allowed. These two features distinguish this model from classical discrete choice models which consider only a single choice among mutually exclusive alternatives. Model parameters are estimated using the simulated method of moments technique. Both requirements - microfoundations and estimability -are imposed in order to exploit the available micro level data on personal computer purchases. The estimated demand structure is used to assess welfare gains from computerization and technological innovation in peripherals industries. The estimated return on investment in computers is 90%. Moreover, a 10% increase in the performance to price ratio of microprocessors leads to a 4% gain in the estimated end user surplus.

Suggested Citation

  • Igal Hendel, 1994. "Estimating Multiple-Discrete Choice Models: An Application to Computeri-zzation Returns," NBER Technical Working Papers 0168, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0168
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    3. Murray Foss & Marylin Manser & Allan Young, 1993. "Price Measurements and Their Uses," NBER Books, National Bureau of Economic Research, Inc, number foss93-1, May.
    4. Kenneth E. Train & Daniel L. McFadden & Moshe Ben-Akiva, 1987. "The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices," RAND Journal of Economics, The RAND Corporation, vol. 18(1), pages 109-123, Spring.
    5. Donald Siegel & Zvi Griliches, 1992. "Purchased Services, Outsourcing, Computers, and Productivity in Manufacturing," NBER Chapters, in: Output Measurement in the Service Sectors, pages 429-460, National Bureau of Economic Research, Inc.
    6. Bresnahan, Timothy F., 1989. "Empirical studies of industries with market power," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 2, chapter 17, pages 1011-1057, Elsevier.
    7. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-561, May.
    8. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    9. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
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    Cited by:

    1. Timothy F. Bresnahan & Scott Stern & Manuel Trajtenberg, 1995. "Market Segmentation and the Sources of Rents from Innovation: Personal Computers in the Late 1980s," Working Papers 95001, Stanford University, Department of Economics.

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