Econometric applications of high-breakdown robust regression techniques

TL;DR

This paper highlights the underuse of high-breakdown robust regression methods in econometrics, demonstrating their effectiveness through re-analysis of existing models and correcting misconceptions about robustness.

Contribution

It introduces new, accessible robust regression techniques from statistics and applies them to econometric models, showing their advantages over traditional methods.

Findings

Traditional methods often fail with non-normal data and outliers.

New robust techniques provide more reliable regression results.

Re-analysis leads to different conclusions from previous studies.

Abstract

A literature search shows that robust regression techniques are rarely used in applied econometrics. We list several misconceptions about robustness which lead to this situation. We show that most data sets are not normal, least squares performs very poorly even in large data sets with small numbers of outliers, and that commonly used techniques for achieving robustness fail to do so. We then provide newly developed techniques from the statistics literature which are easy to understand, and achieve robustness. We show the practical use of these techniques by re-analyzing three regression models from recent literature, and arriving at different conclusions from those reached by the authors.