Identification and estimation of heteroscedastic binary choice models with endogenous dummy regressors

Mu, Beili; Zhang, Zhengyu

doi:10.1111/ectj.12109

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…The modeling of zero-inflated count data with sample selection bias is discussed by Wyszynski and Marra (2018). Mu and Zhang (2018) considered the semiparametric identification and estimation of a heteroscedastic binary choice model with endogenous dummy regressors, and no parametric restriction on the distribution of the error term was assumed. This yields general multiplicative heteroscedasticity in both selection and outcome equations and multiple discrete endogenous regressors.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Heckman Model With Varying Sample Selection Bias and Dispersion Parameters

2023

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Many proposals have emerged as alternatives to the Heckman selection model, mainly to address the non-robustness of its normal assumption. The 2001 Medical Expenditure Panel Survey data is often used to illustrate this non-robustness of the Heckman model. In this paper, we propose a generalization of the Heckman sample selection model by allowing the sample selection bias and dispersion parameters to depend on covariates. We show that the non-robustness of the Heckman model may be due to the assumption of the constant sample selection bias parameter rather than the normality assumption. Our proposed methodology allows us to understand which covariates are important to explain the sample selection bias phenomenon rather than to only form conclusions about its presence. Further, our approach may attenuate the non-identifiability and multicollinearity problems faced by the existing sample selection models. We explore the inferential aspects of the maximum likelihood estimators (MLEs) for our proposed generalized Heckman model. More specifically, we show that this model satisfies some regularity conditions such that it ensures consistency

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Section: Introductionmentioning

confidence: 99%