Bias from Censored Regressors

“Bias from Censored Regressors” with Tom Stoker

We study the bias that arises from using censored regressors in estimation of linear models. We present results on bias in OLS regression estimators with exogenous censoring, and IV estimators when the censored regressor is endogenous. Bound censoring such as top-and bottom-coding result in expansion bias, or effects that are too large. Independent random censoring results in bias that varies with the estimation method; attenuation bias in OLS estimators and expansion bias in IV estimators. We note how large biases can result when there are several regressors, and how that problem is particularly severe when a 0-1 variable is used in place of a continous regressor.

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