Binary Choice Model with Endogeneity: Identification via Heteroskedasticity
19 Pages Posted: 14 Jul 2014
Date Written: July 14, 2014
The idea of identifying structural parameters via heteroskedasticity is explored in the context of binary choice models with an endogenous regressor. Sufficient conditions for parameter identification are derived for probit models without relying on instruments or additional restrictions. The results are extendable to other parametric binary choice models. The semi-parametric model of Manski (1975, 1985), with endogeneity, is also shown to be identifiable in the presence of heteroskedasticity. The role of heteroskedasticity in identifying and estimating structural parameters is demonstrated by Monte Carlo experiments.
Keywords: Qualitative response, Probit, Logit, Linear median regression, Endogeneity, Identification, Heteroskedasticity
JEL Classification: C25, C35, C13
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