Title:      Model Specification in Instrumental-Variables
Regression

Authors:    Thad Dunning

Entrydate:  2008-07-03 10:49:40

Keywords:   Instrumental-Variables Least Squares (IVLS)
regression; model specification; specification error; homogenous
partial effects

Abstract:   In many applications of instrumental-variables
regression, researchers seek to defend the plausibility of a key
assumption: the instrumental variable is independent of the error
term in a linear regression model.  Although fulfilling this
exogeneity criterion is necessary for a valid application of the
instrumental variables approach, it is not sufficient.  In the
regression context, the identification of causal effects depends
not just on the exogeneity of the instrument but also on the
validity of the underlying model. In this paper, I focus on one
feature of such models: the assumption that variation in the
endogenous regressor that is related to the instrumental
variable has the same effect as variation that is unrelated to
the instrument.  In many applications, this assumption may be
quite strong, but relaxing it can limit our ability to estimate
parameters of interest.  After discussing two substantive
examples, I develop analytic results (simulations are reported
elsewhere).  I also present a specification test that may be
useful for determining the relevance of these issues in a given
application.

http://polmeth.wustl.edu/retrieve.php?id=770

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