This paper studies measurement errors that subtract signal from true variables of interest, labeled lack of signal errors (LoSE). The effect on OLS regression of LoSE is opposite the conventional wisdom about classical measurement errors, with LoSE in the dependent variable, not the explanatory...

This note considers a nonlinear regression model containing a 0-1 dichotomous regressor when it is subject to arbitrary measurement errors in the sample. The parameter of interest is the effect of the latent dichotomous variable on the dependent variable. Given that the measurement errors are...

Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when...

This paper considers nonparametric identification and estimation of the regression function when a covariate is mismeasured. The measurement error need not be classical. Employing the small measurement error approximation, we establish nonparametric identification under weak and...

We consider the estimation of nonlinear models with mismeasured explanatory variables, when information on the marginal distribution of the true values of these variables is available. We derive a semi-parametric MLE that is shown to be consistent and asymptotically normally distributed. In a...

The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we...