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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Walter Beckert (2005)
Publisher: MIT Press
Journal: Review of Economics and Statistics
Types: Article
Subjects: ems

Classified by OpenAIRE into

ACM Ref: GeneralLiterature_MISCELLANEOUS
This paper presents a structural econometric framework for discrete and continuous consumer choices in which unobserved intrapersonal and interpersonal preference heterogeneity is modeled explicitly. It outlines a simulation-assisted estimation methodology applicable in this framework. This methodology is illustrated in an application to analyze data from the U.C. Berkeley Internet Demand Experiment. © 2005 President and Fellows of Harvard College and the Massachusetts Institute of Technology.
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    • Ai, C., and X. Chen, “Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions,” Econometrica 71:6 (2003), 1795-1843.
    • Altmann, J., B. Rupp, and P. Varaiya, “Internet Demand under Different Pricing Schemes,” Univ. of California, Berkeley, manuscript (2001).
    • Beckert, W., “On Specification and Identification of Stochastic Demand Models,' Birkbeck College, Univ. of London, manuscript (2004).
    • Blundell, R., “Consumer Behaviour: Theory and Empirical Evidence,” Economic Journal 98 (1988), 16-65.
    • Blundell, R., X. Chen, and D. Kristensen, “Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves,” Institute for Fiscal Studies, London, CEMMAP working paper (2003).
    • Blundell, R., and R. J. Smith, “Coherency and Estimation in Simultaneous Models with Censored or Qualitative Dependent Variables,” Journal of Econometrics 64 (1994), 355-373.
    • Brown, B. W., and M. B. Walker, “The Random Utility Hypothesis and Inference in Demand Systems,” Econometrica 59 (1989), 925-951.
    • Chib, S., and E. Greenberg, “Markov Chain Monte Carlo Simulation Methods in Econometrics,” Econometric Theory 49:4 (1996), 327- 335.
    • Darolles, S., J. P. Florens, and E. Renault, “Nonparametric Instrumental Variables Regression,” GREMAQ, University of Toulouse, mimeograph (2002).
    • Das, M., W. K. Newey, and F. Vella, “Nonparametric Estimation of Sample Selection Models,” Review of Economic Studies 70:1 (2003), 33-58.
    • Dubin, J. A., Consumer Durable Choice and the Demand for Electricity (Amsterdam: North-Holland, 1985).
    • Dubin, J. A., and D. L. McFadden, “An Econometric Analysis of Residential Electric Appliance Holdings and Consumption,” Econometrica 52:2 (1984), 345-362.
    • Edell, R., and P. Varaiya, “Providing Internet Access: What We Learn from INDEX,' Univ. of California, Berkeley, manuscript (1999).
    • Hajivassiliou, V. A., and P. A. Ruud, “Classical Estimation Methods for LDV Models Using Simulation” (pp. 2382-2441), in R. F. Engle and D. L. McFadden (Eds.), Handbook of Econometrics 4 (Amsterdam: North-Holland, 1994).
    • Hall, P., and J. Horowitz, “Nonparametric Methods for Inference in the Presence of Instrumental Variables,” Department of Economics, Northwestern University, working paper (2003).
    • Lewbel, A., “Demand Systems With and Without Errors,” American Economic Review 91:3 (2001), 611-618.
    • McFadden, D. L., “The Measurement of Urban Travel Demand,” Journal of Public Economics 3:4 (1974), 303-328.
    • “A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,” Econometrica 57:5 (1989), 995-1026.
    • McFadden, D. L., and P. A. Ruud, “Estimation by Simulation,” this REVIEW, 76:4 (1994), 591-608.
    • Newey, W. K., and J. L. Powell, “Instrumental Variables Estimation for Nonparametric Models,” Econometrica 71:5 (2003), 1565-1678.
    • Newey, W. K., J. L. Powell, and F. Vella, “Nonparametric Estimation of Triangular Simultaneous Equations Models,” Econometrica 67:3, (1999), 565-604.
    • Pakes, A., and D. Pollard, “Simulation and the Asymptotics of Optimization Estimators,' Econometrica 57:5 (1989), 1027-1057.
    • Rupp, B., R. Edell, H. Chand, and P. Varaiya, “INDEX: A Platform for Determining How People Value the Quality of Their Internet Access,” in Proceedings of the 6th IEEE/IFIP International Workshop on Quality of Service (Piscataway, NJ: IEEE Press, 1998).
    • Rust, J., “Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher,” Econometrica 55:5 (1987), 999-1033. “Structural Estimation of Markov Decision Processes” (pp. 3081- 3143), in R. F. Engle and D. L. McFadden (Eds.), Handbook of Econometrics 4 (Amsterdam: North-Holland, 1994).
    • Varian, H. R., “Estimating the Demand for Bandwidth,' School for Information Management and Systems, Univ. of California, Berkeley, manuscript (2000).
    • Wilson, R. B., Nonlinear Pricing (Oxford: Oxford University Press, 1993).
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