We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Sharp Identification Regions in Models With Convex Moment Predictions.
- Authors
Beresteanu, Arie; Molchanov, Ilya; Molinari, Francesca
- Abstract
We provide a tractable characterization of the sharp identification region of the parameter 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 convex moment predictions. Examples include static, simultaneous-move finite games of complete and incomplete information in the presence of multiple equilibria; best linear 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 Θ I, 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 efficiently verify whether a candidate θ is in Θ I. We use examples analyzed in the literature to illustrate the gains in identification and computational tractability afforded by our method.
- Subjects
ECONOMETRIC models; VECTOR analysis; MATHEMATICAL models of economic forecasting; ECONOMIC equilibrium; MATHEMATICAL models; CONVEX programming; FINITE groups
- Publication
Econometrica, 2011, Vol 79, Issue 6, p1785
- ISSN
0012-9682
- Publication type
Article
- DOI
10.3982/ECTA8680