Identification of Joint Distributions with Applications to the Roy Model and Competing Auctions
نویسندگان
چکیده
The paper presents conditions under which Sklar’s copula formula can be used to approximate the full joint distribution from observed marginal distributions and a partially observed joint distribution. The first part presents conditions under which the parameters of a finite polynomial copula function are uniquely determined. The second part presents conditions under which the joint probability of interest can be arbitrarily approximated by a known polynomial of the observed marginal probabilities. The result is applied to the extended Roy model providing an alternative approach to non-parametric identification. The result is also applied to identifying value distribution for differentiated goods using auction platform data. ∗Thanks to Matthew Chesnes, Eric French, Ian Gale, Bruce Hansen, James Heckman, Hiro Kasahara, Kyoo il Kim, Greg Lewis, Rob McMillan, Chris Metcalf, Byoung Park, Katja Seim, Art Shneyerov, Nathan Wilson, Jeff Wooldridge, Robert Zeithammer, David Zimmer, participants at the 2009 IIOC, Wisconsin, Minnesota, DOJ, Cornell Johnson School, Duke, as well as the many others that I have talked to about this question. I am also grateful to an anonymous reviewer who pointed to the connection between the auction problem and the Roy model. The views expressed in this article are those of the author and do not necessarily reflect those of the Federal Trade Commission. All remaining errors are my own.
منابع مشابه
Identification in the Roy Model with an Application to Auctions
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