Censored Regressors and Expansion Bias
Author(s)
Rigobon, Roberto
Stoker, Thomas M.
Date Issued
March 12, 2004
Series/Report no.
MIT Sloan School of Management Working Paper;4451-03
Abstract
We show how using censored regressors leads to expansion bias, or estimated effects that are proportionally too large. We show the necessity of this effect in bivariate regression and illustrate the bias using results for normal regressors. We study the bias when there is a censored regressor among many regressors, and we note how censoring can work to undo errors-in-variables bias. We discuss several approaches to correcting expansion bias. We illustrate the concepts by considering how censored regressors can arise in the analysis of wealth effects on consumption, and on peer effects in productivity.
Subjects
Censored Regressors
Expansion Bias
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