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 paper investigates the effects of using residuals from robust regression in place of OLS residuals in test statistics for the normality of the errors. It is found that for systematic and clustered outliers robustified normality tests yield greater power

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...

This paper develops a wavelet (spectral) approach to estimate the parameters of a linear regression model where the regressand and the regressors are persistent processes and contain a measurement error. We propose a wavelet filtering approach which does not require instruments and yields...