http://www.econometricsociety.org/ Econometrica, Vol. 79, No. 6 (November, 2011), 1785–1821 SHARP IDENTIFICATION REGIONS IN MODELS WITH CONVEX MOMENT PREDICTIONS ARIE BERESTEANU University of Pittsburgh, Pittsburgh, PA 15260, U.S.A. ILYA MOLCHANOV Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland FRANCESCA MOLINARI Cornell University, Ithaca, NY 14853-7601, U.S.A. The copyright to this Article is held by the Econometric Society. It may be downloaded, printed and reproduced only for educational or research purposes, including use in course packs. No downloading or copying may be done for any commercial purpose without the explicit permission of the Econometric Society. For such commercial purposes contact the Office of the Econometric Society (contact information may be found at the website http://www.econometricsociety.org or in the back cover of Econometrica). This statement must be included on all copies of this Article that are made available electronically or in any other format. Econometrica, Vol. 79, No. 6 (November, 2011), 1785–1821 SHARP IDENTIFICATION REGIONS IN MODELS WITH CONVEX MOMENT PREDICTIONS BY ARIE BERESTEANU,ILYA MOLCHANOV, AND FRANCESCA MOLINARI1 We provide a tractable characterization of the sharp identification region of the pa- rameter vector θ in a broad class of incomplete econometric models. Models in this class have set-valued predictions that yield a convex set of conditional or unconditional moments for the observable model variables. In short, we call these models with con- vex moment predictions. Examples include static, simultaneous-move finite games of complete and incomplete information in the presence of multiple equilibria; best lin- ear predictors with interval outcome and covariate data; and random utility models of multinomial choice in the presence of interval regressors data. Given a candidate value for θ, we establish that the convex set of moments yielded by the model predictions can be represented as the Aumann expectation of a properly defined random set. The sharp identification region of θ, denoted ΘI , can then be obtained as the set of minimizers of the distance from a properly specified vector of moments of random variables to this Aumann expectation. Algorithms in convex programming can be exploited to effi- ciently verify whether a candidate θ is in ΘI We use examples analyzed in the literature to illustrate the gains in identification and computational tractability afforded by our method. KEYWORDS: Partial identification, random sets, Aumann expectation, support func- tion, finite static games, multiple equilibria, random utility models, interval data, best linear prediction. 1. INTRODUCTION Overview THIS PAPER PROVIDES a simple, novel, and computationally feasible procedure to determine the sharp identification region of the parameter vector θ that characterizes a broad class of incomplete econometric models. Models in this class have set-valued predictions which yield a convex set of conditional or un- conditional moments for the model observable variables. In short, throughout the paper, we call these models with convex moment predictions. Our use of the 1This paper supersedes two earlier working papers (Beresteanu, Molchanov, and Molinari (2008, 2009)). We thank a co-editor and three anonymous reviewers for very useful suggestions that substantially improved the paper. We are grateful to H. Bar, T. Bar, L. Barseghyan, I. Canay, G. Chamberlain, P. Ellickson, Y. Feinberg, K. Hirano, T. Magnac, T. O’Donoghue, M. Peski, A. Rosen, X. Shi, J. Stoye, A. Sweeting, and, especially, L. Blume, D. Easley, and C. Manski for comments. We also thank seminar participants at many institutions and conferences where this paper was presented. We are grateful to M. Grant for advice on the use of the CVX software, which we used to run simulations in MatLab. Financial support from the U.S. NSF Grants SES- 0617559 and SES-0922373 (Beresteanu) and SES-0617482 and SES-0922330 (Molinari); from the Swiss National Science Foundation Grants 200021-117606 and 200021-126503 (Molchanov); and from the Center for Analytic Economics at Cornell University is gratefully acknowledged. Ilya Molchanov gratefully acknowledges the hospitality of the Department of Economics at Cornell University in October 2006. © 2011 The Econometric Society DOI: 10.3982/ECTA8680 1786 A. BERESTEANU, I. MOLCHANOV, AND F. MOLINARI term “model” encompasses econometric frameworks ranging from structural semiparametric models to nonparametric best predictors under square loss. In the interest of clarity of exposition, in this paper we focus on the semiparamet- ric case. We exemplify our methodology by applying it to static, simultaneous- move finite games of complete and incomplete information in the presence of multiple equilibria; best linear predictors with interval outcome and covari- ate data; and random utility models of multinomial choice in the presence of interval regressors data. Models with convex moment predictions can be described as follows. For a given value of the parameter vector θ and realization of (a subset of) model variables, the economic model predicts a set of values for a vector of vari- ables of interest. These are the model set-valued predictions, which are not necessarily convex. No restriction is placed on the manner in which, in the data generating process, a specific model prediction is selected from this set. When the researcher takes conditional expectations of the resulting elements of this set, the unrestricted process of selection yields a convex set of moments for the model variables—this is the model’s convex set of moment predictions.If this set were almost surely single valued, the researcher would be able to iden- tify θ by matching the model-implied vector of moments to the one observed in the data. When the model’s moment predictions are set-valued, one may find many values for the parameter vector θ which, when coupled with specific selection mechanisms picking one of the model set-valued predictions, gener- ate the same conditional expectation as the one observed in the data. Each of these values of θ is observationally equivalent, and the question becomes how to characterize the collection of observationally equivalent θ’sinatractable manner. Although previous literature has provided tractable characterizations of the sharp identification region for certain models with convex moment predictions (see, e.g., Manski (2003) for the analysis of nonparametric best predictors un- der square loss with interval outcome data), there exist many important prob- lems, including the examples analyzed in this paper, in which such a character- ization is difficult to obtain. The analyzes of Horowitz, Manski, Ponomareva, and Stoye (2003; HMPS henceforth), and Andrews, Berry, and Jia (2004;ABJ henceforth), and Ciliberto and Tamer (2009; CT henceforth) are examples of research studying, respectively, the identified features of best linear predic- tors with missing outcome and covariate data, and finite games with multiple pure strategy Nash equilibria. HMPS provided sharp identification regions, but these may have prohibitive computational cost. To make progress not only on identification analysis, but also on finite sample inference, ABJ and CT pro- posed regions of parameter values which are not sharp. Establishing whether a conjectured region for the identified features of an incomplete econometric model is sharp is a key step in identification analysis. Given the joint distribution of the observed variables, a researcher asks herself what parameters θ are consistent with this distribution. The sharp identifica- tion region is the collection of parameter values that could generate the same SHARP IDENTIFICATION REGIONS 1787 distribution of observables as the one in the data, for some data generating process consistent with the maintained assumptions. Examples of sharp iden- tification regions for parameters of incomplete models are given in Manski (1989, 2003), Manski and Tamer (2002),andMolinari (2008), among others. In some cases, researchers are only able to characterize a region in the para- meter space that includes all the parameter values that may have generated the observables, but may include other (infeasible) parameter values as well. These larger regions are called outer regions. The inclusion in the outer regions of parameter values which are infeasible may weaken the researcher’s ability to make useful predictions and to test for model misspecification. Using the theory of random sets (Molchanov (2005)), we provide a general methodology that allows us to characterize the sharp identification region for the parameters of models with convex moment predictions in a computation- ally tractable manner. Our main insight is that for a given candidate value of θ, the (conditional or unconditional) Aumann expectation of a properly defined θ-dependent random closed set coincides with the convex set of model moment predictions. That is, this Aumann expectation gives the convex set, implied by the candidate θ of moments for the relevant variables which are consistent with all the model’s implications.2 This is a crucial advancement compared to the related literature, where researchers are often unable to fully exploit the information provided by the model that they are studying and work with just a subset of the model’s implications. In turn, this advancement allows us to characterize the sharp identification region of θ, denoted ΘI through a simple necessary and sufficient