A Practical Comparison of the Bivariate Probit and Linear IV Estimators
This paper presents asymptotic theory and Monte-Carlo simulations comparing maximum-likelihood bivariate probit and linear instrumental variables estimators of treatment effects in models with a binary endogenous treatment and binary outcome. The t...
| Main Authors: | , , |
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| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://www-wds.worldbank.org/external/default/main?menuPK=64187510&pagePK=64193027&piPK=64187937&theSitePK=523679&menuPK=64187510&searchMenuPK=64187283&siteName=WDS&entityID=000158349_20110317174628 http://hdl.handle.net/10986/3368 |
| Summary: | This paper presents asymptotic theory
and Monte-Carlo simulations comparing maximum-likelihood
bivariate probit and linear instrumental variables
estimators of treatment effects in models with a binary
endogenous treatment and binary outcome. The three main
contributions of the paper are (a) clarifying the
relationship between the Average Treatment Effect obtained
in the bivariate probit model and the Local Average
Treatment Effect estimated through linear IV; (b) comparing
the mean-square error and the actual size and power of tests
based on these estimators across a wide range of parameter
values relative to the existing literature; and (c)
assessing the performance of misspecification tests for
bivariate probit models. The authors recommend two changes
to common practices: bootstrapped confidence intervals for
both estimators, and a score test to check goodness of fit
for the bivariate probit model. |
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