Endogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference
For binary outcome models, an endogeneity correction based on nonlinear rank-based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic normality. Monte Carlo simulations and an application on German insolvency data illustrate the usefulness of the method.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Asymptotic Properties of Endogeneity Corrections Using Nonlinear Transformations
This paper considers a linear regression model with an endogenous regressor which arises from a nonlinear transformation of a latent variable. It is shown that the corresponding coefficient can be consistently estimated …
regressionBGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis
Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high-dimensional covariates. Existing nonlinear IV methods directly…
Identification of Regression Models with a Misclassified and Endogenous Binary Regressor
We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonpa…
regressionMarginal Effects for Probit and Tobit with Endogeneity
When evaluating partial effects, it is important to distinguish between structural endogeneity and measurement errors. In contrast to linear models, these two sources of endogeneity affect partial effects differently in …
validIV Regressions without Exclusion Restrictions
We study identification and estimation of endogenous linear and nonlinear regression models without excluded instrumental variables, based on the standard mean independence condition and a nonlinear relevance condition. …
regression