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On recombinant estimation for experimental data

Published online by Cambridge University Press:  14 March 2025

Jason Abrevaya
Jason Abrevaya*
Affiliation:
Department of Economics, Purdue University, 403 W. State St., West Lafayette, IN 47907-2056
*
e-mail: abrevaya@purdue.edu
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Abstract

The recombinant estimation technique of Mullin and Reiley (2006) can be a useful tool for analyzing data from normal-form games. The recombinant estimator falls within a general category of statistics known as U-statistics. This classification has both theoretical and practical implications: (1) the recombinant estimator is optimal (minimum variance) among unbiased estimators, (2) there is a computationally simple method for computing its asymptotic standard error, and (3) the estimation technique can be extended to multiple outcomes and to other types of inferential procedures commonly used for experimental data, such as the sign test. Simulation evidence suggests that researchers should use the asymptotic standard error rather than the standard error of Mullin and Reiley (2006) since the latter exhibits a downward bias.

Keywords

Recombinant estimationU-statistics

JEL classification

C12: Hypothesis Testing: General C90: General

Information

Type
Research Article
Information
Experimental Economics , Volume 11 , Issue 1 , March 2008 , pp. 25 - 52
Copyright
Copyright © 2006 Economic Science Association

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References

Apesteguia, J., Dufwenberg, M., & Selten, R. (Forthcoming), Blowing the whistle. Economic Theory.Google Scholar
Chen, Y., & Sönmez, T. (2002). Improving efficiency of on-campus housing: an experimental study. American Economic Review, 92, 1669–1686.CrossRefGoogle Scholar
Chen, Y., & Sönmez, T. (2006). School choice: an experimental study. Journal of Economic Theory, 127, 202–231.CrossRefGoogle Scholar
Dufwenberg, M., Gneezy, U., Goeree, J. K., & Nagel, R. (Forthcoming). Price floors and competition. Economic Theory.Google Scholar
List, J., & Lucking-Reiley, D. (2000). Demand reduction in multi-unit auctions: evidence from a sportscard field experiment. American Economic Review, 90, 961–972.CrossRefGoogle Scholar
Mehta, J., Starmer, C., & Sugden, R. (1993). The nature of salience: an experimental investigation of pure coordination games. American Economic Review, 84, 658–673.Google Scholar
Mitzkewitz, M., & Nagel, R. (1993). Experimental results on ultimatum games within complete information. International Journal of Game Theory, 22, 171–198.10.1007/BF01243649CrossRefGoogle Scholar
Mullin, C. H., & Reiley, D. (2006). Recombinant estimation for normal-form games with applications to auctions and bargaining. Games and Economic Behavior, 54, 159–182.CrossRefGoogle Scholar
Serfling, R. J. (1980). Approximation theorems of mathematical statistics. John Wiley & Sons.CrossRefGoogle Scholar
van der Vaart, A. W. (1998). Asymptotic statistics. Cambridge University Press.CrossRefGoogle Scholar