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 National Science
Foundation Award #0551272 |
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Identification and Estimation in Structural Econometric Models |
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| Investigator(s): |
Rosa Matzkin (PI)
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| Sponsor: |
Northwestern University, IL 60208 8474913003
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| Start Date/Expiration Date |
2006-03-01 to 2007-02-28 (amended 2006-02-14) |
| Awarded Amount to Date: |
$61,942 |
| Abstract: Structural econometric models are essential tools for many empirical studies in fields such as
industrial organization, labor and public economics, and development economics. The analysis
of identification is a first necessary step in any such studies. In many of these models, such as
those involving multidimensional optimization or equilibrium conditions, the variables of interest
are determined simultaneously. A recent result by Benkard and Berry (2004) has shown that
identification results that were used for a long time to determine identification, in simultaneous
equations models, are incorrect. Hence, other than in the restrictive linear specification models
with additive unobservable random terms, studied decades ago, little is known at present about
the conditions for identification in structural simultaneous equation models.
In this project, the PI will develop correct conditions for identification of systems of simultaneous
equations, in parametric and nonparametric models, with additive and nonadditive unobservable
variables. Since a large body of previous work in econometrics has relied on the previous incorrect
conditions, the PI will also analyze under what additional conditions in the structural models, those
previous results still hold.
The identification conditions that will be developed will be used to guide the discovery of
new 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 and
understanding of the new methods, she will consider applications to several leading models in
applied 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 Activity
The results about identification of simultaneous equations that the PI will develop will allow
applied econometricians to determine the elements that can be identified in an econometric
model, given their available data. The results will be applicable to very general models, which
do not specify parametric structures either for the unknown functions or for the distributions of
the unobservable random terms, as well as to more restrictive, parametric models. The results
about estimation of nonparametric models will allow researchers to estimate such models without
imposing parametric restrictions.
Since structural econometric models where the values of the variables of interest are determined
simultaneously is widespread in most applied fields in economics, these results are predicted to
have a very wide impact. Moreover, by opening the road to new ways of analyzing identification
and estimation in nonparametric simultaneous equation models, it is expected that a wave of new
theoretical results will follow, as a result of the research in this project.
Broader Impacts
Models 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 this
project will be suitable for applications in these sciences, and through them, they will benefit society
at large. With this aim, the results of the project will be disseminated widely. By involving a
graduate student in this research, it is expected that he/she will apply the new results in his/her
dissertation and/or develop new related results. |
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| NSF Org: |
SES - Division of Social and Economic Sciences |
| Award Number: |
0551272 |
| Award Instrument: |
Continuing grant |
| Program Manager: |
Daniel H. Newlon
SES Division of Social and Economic Sciences
SBE Directorate for Social, Behavioral & Economic Sciences
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| NSF Program(s): |
ECONOMICS |
| Field Application(s): |
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| Program Reference Code(s): |
UNASSIGNED, 0000 |
| Program Element Code(s): |
1320 |
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