Identification and Estimation in Structural Econometric Models

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Structural econometric models are essential tools for many empirical studies in fields such asindustrial organization, labor and public economics, and development economics. The analysisof identification is a first necessary step in any such studies. In many of these models, such asthose involving multidimensional optimization or equilibrium conditions, the variables of interestare determined simultaneously. A recent result by Benkard and Berry (2004) has shown thatidentification results that were used for a long time to determine identification, in simultaneousequations models, are incorrect. Hence, other than in the restrictive linear specification modelswith additive unobservable random terms, studied decades ago, little is known at present aboutthe conditions for identification in structural simultaneous equation models.In this project, the PI will develop correct conditions for identification of systems of simultaneousequations, in parametric and nonparametric models, with additive and nonadditive unobservablevariables. Since a large body of previous work in econometrics has relied on the previous incorrectconditions, the PI will also analyze under what additional conditions in the structural models, thoseprevious results still hold.The identification conditions that will be developed will be used to guide the discovery ofnew methods for estimation in nonparametric simultaneous equations, which will be consistent,asymptotically normal, and easy to compute.Since in many of the structural econometric models encountered in applied fields in economics,one encounters situations where observations on the actual values of endogenous variables, such as profits or utility values, is limited, the identification and estimation results will be extended to such situations, where the endogenous variables is simultaneous equations models are latent.To guide the PI in the development of the new methods, and to facilitate the adoption andunderstanding of the new methods, she will consider applications to several leading models inapplied economics, such as models of consumer demand, discrete choice models, hedonic equilibrium models, Nash equilibrium, and models of survey response errors.Intellectual Merit of the Proposed ActivityThe results about identification of simultaneous equations that the PI will develop will allowapplied econometricians to determine the elements that can be identified in an econometricmodel, given their available data. The results will be applicable to very general models, whichdo not specify parametric structures either for the unknown functions or for the distributions ofthe unobservable random terms, as well as to more restrictive, parametric models. The resultsabout estimation of nonparametric models will allow researchers to estimate such models withoutimposing parametric restrictions.Since structural econometric models where the values of the variables of interest are determinedsimultaneously is widespread in most applied fields in economics, these results are predicted tohave a very wide impact. Moreover, by opening the road to new ways of analyzing identificationand estimation in nonparametric simultaneous equation models, it is expected that a wave of newtheoretical results will follow, as a result of the research in this project.Broader ImpactsModels where several variables of interest are determined simultaneously are widespread in,among others, engineering and the social sciences. The methods that will be developed in thisproject will be suitable for applications in these sciences, and through them, they will benefit societyat large. With this aim, the results of the project will be disseminated widely. By involving agraduate student in this research, it is expected that he/she will apply the new results in his/herdissertation and/or develop new related results.