Robust Regression

Zaman, Asad, Peter J. Rousseeuw, and Mehmet Orhan. "Econometric applications of high-breakdown robust regression techniques." Economics Letters 71.1 (2001): 1-8.

A Zaman, PJ Rousseeuw, M Orhan - Economics Letters, 2001 - Elsevier

A literature search shows that robust regression techniques are rarely used in applied

econometrics. We present a technique based on Rousseeuw and Van Zomeren [Journal of

the American Statistical Association, 85 (1990) 633–639] that removes many of the ...

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1. Introduction

Since it is well-established that even high quality data tend to contain outliers, and it is a rare

economic data set that qualifies as ‘high-quality’, one would expect far greater reliance on robust

regression techniques than is actually observed. Searching through ECONLIT for the term ‘robust

regression’ led to a remarkably low total of 14 papers published in different journals most of

which apply robust regression techniques to real economic data sets. We believe that this reluctance

to use robust regression techniques may be due to the following factors.

 The belief that large sample sizes make robust techniques unnecessary — with enough data,

one can find the truth.

 The belief that outliers can be detected simply by eye, or by looking for unusual OLS

residuals, or by sensitivity analysis, obviating the need for a robust analysis.

 Existence of several ‘robust regression’ techniques with little guidance available as to which

is appropriate.

 Unfamiliarity with interpretation of results from a robust analysis.

 Unawareness of gains available from robust analysis in real data sets.

In this paper we hope to remove some of these barriers and facilitate more routine use of a particular

set of robust regression procedures, based on Rousseeuw and Van Zomeren (1990) and modern

algorithms.