Prediction in linear index models with endogenous regressors
Christopher L. Skeels
Department of Economics
University of Melbourne
Melbourne, Australia
[email protected]
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Larry W. Taylor
Department of Economics
Lehigh University
Bethlehem, PA
[email protected]
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Abstract. In this article, we examine prediction in the context of linear index models
when one or more of the regressors are endogenous. To facilitate both
within-sample and out-of-sample predictions, Stata offers the postestimation
command predict (see [R] predict). We believe that the
usefulness of the predictions provided by this command is limited, especially
if one is interested in out-of-sample predictions. We demonstrate our point
using a probit model with continuous endogenous regressors, although it
clearly generalizes readily to other linear index models. We subsequently
provide a program that offers one possible implementation of a new command,
ivpredict, that can be used to address this shortcoming of
predict, and we then illustrate its use with an empirical example.
View all articles by these authors:
Christopher L. Skeels, Larry W. Taylor
View all articles with these keywords:
predict, probit, logit, ivprobit, prediction, linear index, endogenous regressors, ivpredict, out-of-sample prediction
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