Bias From Censored Regressors
研究在线性模型估计中使用删失回归变量导致的偏误，发现外生删失时OLS估计量产生膨胀偏误，内生删失时OLS产生衰减偏误而IV产生膨胀偏误，多个回归变量或使用0-1变量替代连续变量时偏误更严重。
We study the bias that arises from using censored regressors in estimation of linear models. We present results on bias in ordinary least aquares (OLS) regression estimators with exogenous censoring and in instrumental variable (IV) estimators when the censored regressor is endogenous. Bound censoring such as top-coding results in expansion bias, or effects that are too large. Independent censoring results in bias that varies with the estimation method—attenuation bias in OLS estimators and expansion bias in IV estimators. Severe biases can result when there are several regressors and when a 0–1 variable is used in place of a continuous regressor.