Simple Estimators For Hard Problems: Endogeneity in Discrete Choice Related Models
This paper describes numerically simple estimators that can be used to estimate binary choice and other related models (such as selection and ordered choice models) when some regressors are endogenous or mismeasured. Simple estimators are provided that allow for discrete or otherwise limited endogenous regressors, lagged dependent variables and other dynamic effects, heteroskedastic and autocorrelated latent errors, and latent fixed effects.
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