The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can...

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to...

This paper constructs estimators for panel data regression models with individual specific heterogeneity and two-sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re-censored or re-truncated residuals. While these...

Econometric analyses in the happiness literature typically use subjective well-being (SWB) data to compare the mean of observed or latent happiness across samples. Recent critiques show that com-paring the mean of ordinal data is only valid under strong assumptions that are usually rejected by...

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

Linear regression is widely-used in finance. While the standard method to obtain parameter estimates, Least Squares, has very appealing theoretical and numerical properties, obtained estimates are often unstable in the presence of extreme observations which are rather common in financial time...

We present asymptotic power-one tests of regression model functional form for heavy tailed time series. Under the null hypothesis of correct specification the model errors must have a finite mean, and otherwise only need to have a fractional moment. If the errors have an infinite variance then...

It is well known that consistent estimators of errors-in-variables models require knowledge of the ratio of error variances. What is not well known is that a Joint Least Squares estimator is robust to a wide misspecification of that ratio. Through a series of Monte Carlo experiments we show that...

Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original...

The paper focuses on standard error estimation in FE models if there is serial correlation in the error process. Applied researchers have often ignored the problem, probably because major statistical packages do not estimate robust standard errors in FE models. Not surprisingly, this can lead to...