Hostname: page-component-745bb68f8f-b95js Total loading time: 0 Render date: 2025-02-11T14:18:42.433Z Has data issue: false hasContentIssue false
  • English
  • Français

Identification, Estimation, and Testing in Parametric Empirical Models of Auctions within the Independent Private Values Paradigm

Published online by Cambridge University Press:  11 February 2009

Stephen G. Donald  and
Harry J. Paarsch
Stephen G. Donald
Affiliation:
Boston University
Harry J. Paarsch
Affiliation:
University of Western Ontario
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent advances in the application of game theory to the study of auctions have spawned a growing empirical literature involving both experimental and field data. In this paper, we focus on four different mechanisms (the Dutch, English, first-price sealed-bid, and Vickrey auctions) within one of the most commonly used theoretical models (the independent private values paradigm) to investigate issues of identification, estimation, and testing in parametric structural econometric models of auctions.

Type
Research Article
Information
Econometric Theory , Volume 12 , Issue 3 , August 1996 , pp. 517 - 567
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Amemiya, T. (1985) Advanced Econometrics. Cambridge: Harvard University Press.Google Scholar
Coppinger, V., Smith, V., & Titus, J. (1980) Incentives and behavior in English, Dutch, and sealed-bid auctions. Economic Inquiry 18, 1–22.CrossRefGoogle Scholar
Donald, S. & Paarsch, H. (1992a) Maximum Likelihood Estimation in Empirical Models of Auctions. Research report 9211, Department of Economics, University of Western Ontario.Google Scholar
Donald, S. & Paarsch, H. (1992b) Identification in Empirical Models of Auctions. Research report 9216, Department of Economics, University of Western Ontario.Google Scholar
Donald, S. & Paarsch, H. (1993) Piecewise pseudo-maximum likelihood estimation in empirical models of auctions. International Economic Review 34, 121–148.CrossRefGoogle Scholar
Engle, R. (1984) Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. In Griliches, Z. & Intriligator, M. (eds.), Handbook of Econometrics, vol. II, pp. 775–826. Amsterdam: North-Holland.CrossRefGoogle Scholar
Flinn, C. & Heckman, J. (1982) New methods for analyzing structural models of labor force dynamics. Journal of Econometrics 18, 115–168.CrossRefGoogle Scholar
Galambos, J. (1978) The Asymptotic Theory of Extreme Order Statistics. New York: Wiley.Google Scholar
Gallant, R. & Nychka, D. (1987) Semi-nonparametric maximum likelihood estimation. Econo-metrica 55, 363–390.CrossRefGoogle Scholar
Gallant, R. & Tauchen, G. (1992) Which Moments to Match? Unpublished manuscript, Department of Economics, Duke University.Google Scholar
Guerre, E., Perrigne, I., & Vuong, Q. (1995) Nonparametric Estimation of First-Price Auctions. Unpublished manuscript, Department of Economics, University of Southern California.Google Scholar
Hall, B., Schnake, R., & Cummins, C. (1988) TSP Reference Manual, vers. 4.1. Palo Alto, CA: TSP International.Google Scholar
Han, S. (1976) Superlinearly convergent variable metric algorithms for general nonlinear programming problems. Mathematical Programming 11, 263–282.CrossRefGoogle Scholar
Han, S. (1977) A globally convergent method for nonlinear programming. Journal of Optimization Theory and Applications 22, 297–309.CrossRefGoogle Scholar
Hansen, R. (1986) Sealed-bid versus open auctions: the evidence. Economic Inquiry 24, 125–142.CrossRefGoogle Scholar
Hoadley, B. (1971) Asymptotic properties of maximum likelihood estimators for the independent not identically distributed case. Annals of Mathematical Statistics 42, 1977–1991.CrossRefGoogle Scholar
Jennrich, R. (1969) Asymptotic properties of non-linear least squares estimators. Annals of Mathematical Statistics 40, 633–643.CrossRefGoogle Scholar
Johnson, R. (1979) Oral auction versus sealed bids: an empirical investigation. Natural Resources Journal 19, 315–335.Google Scholar
Laffont, J.-J., Ossard, H., & Vuong, Q. (1995) Econometrics of first-price auctions. Economet-rica 63, 953–980.CrossRefGoogle Scholar
LeCam, L. (1953) On some asymptotic properties of maximum likelihood estimates and related Bayes estimates. University of California Publications in Statistics 1, 277–330.Google Scholar
Mangasarian, O. (1969) Nonlinear Programming. New York: McGraw-Hill.Google Scholar
Mead, W. (1967) Natural resource disposal policy-Oral auction versus sealed bids. Natural Resources Journal!, 194–224.Google Scholar
Milgrom, P. (1985) The economics of competitive bidding: A selective survey. In Hurwicz, L.Schmeidler, D., & Sonnenschein, H. (eds.). Social Goals and Social Organization, pp. 261–289. Cambridge: Cambridge University Press.Google Scholar
Milgrom, P. (1987) Auction theory. In Bewley, T. (ed.), Advances in Economic Theory, pp. 1–32. Cambridge: Cambridge University Press.Google Scholar
Milgrom, P. & Weber, R. (1982) A theory of auctions and competitive bidding. Econometrica 50, 1089–1122.CrossRefGoogle Scholar
Paarsch, H. (1989) Empirical Models of Auctions within the Independent Private Values Paradigm and an Application to British Columbian Timber Sales. Discussion paper 89–14, Department of Economics, University of British Columbia.Google Scholar
Paarsch, H. (1991) Empirical Models of Auctions with an Application to British Columbian Timber Sales. Discussion paper 91–19, Department of Economics, University of British Columbia.Google Scholar
Paarsch, H. (1992) Deciding between the common and private value paradigms in empirical models of auctions. Journal of Econometrics 51, 191–215.CrossRefGoogle Scholar
Paarsch, H. (1993) Deriving an Estimate of the Optimal Reserve Price: An Application to British Columbian Timber Sales. Unpublished manuscript, Department of Economics, University of Western Ontario.Google Scholar
Paarsch, H. (1994) A comparison of estimators for empirical models of auctions. Annales d'Economie et de Statistique 34, 142–157.Google Scholar
Powell, M. (1978) A fast algorithm for nonlinearly constrained optimization calculations. In Watson, G. (ed.), Numerical Analysis. Proceedings of the Biennial Conference Held at Dundee, June 1977. Lecture Notes in Mathematics, vol. 630, pp. 144–157. Berlin: Springer.Google Scholar
Prakasa-Rao, B. (1987) Asymptotic Theory of Statistical Inference. New York: Wiley.Google Scholar
Reiss, R.-D. (1989) Approximate Distributions of Order Statistics: With Applications to Non-parametric Statistics. New York: Springer-Verlag.CrossRefGoogle Scholar
Riley, J. & Samuelson, W. (1981) Optimal auctions. American Economic Review 71, 381–392.Google Scholar
Schittkowski, K. (1981a) The nonlinear programming method of Wilson, Han, and Powell with an augmented Lagrangian type line search function. Part 1: Convergence analysis. Numerische Mathematik 38, 83–114.CrossRefGoogle Scholar
Schittkowski, K. (1981b) The nonlinear programming method of Wilson, Han, and Powell with an augmented Lagrangian type line search function. Part 2: An efficient implementation with linear least squares subproblems. Numerische Mathematik 38, 115–127.CrossRefGoogle Scholar
Smith, V. (1982) Microeconomic systems as an experimental science. American Economic Review 72, 923–955.Google Scholar
Vaessen, W. (1984) UBC NLP: Nonlinear Function Optimization. Vancouver, Canada: University of British Columbia Computing Centre.Google Scholar
Vickrey, W. (1961) Counterspeculation, auctions, and competitive sealed tenders. Journal of Finance 16, 8–37.CrossRefGoogle Scholar
Wald, A. (1949) Note on the consistency of the maximum likelihood estimate. Annals of Mathematical Statistics 20, 595–601.CrossRefGoogle Scholar
White, H. (1982) Maximum likelihood estimation of misspecified models. Econometrica 50, 1–25.CrossRefGoogle Scholar
Wilson, R. (1963) A Simplicial Algorithm for Concave Programming. Unpublished Ph.D. dissertation, Harvard Business School, Harvard University.Google Scholar