Testing the Presence of Outliers in Regression Models

65 Pages Posted: 8 Aug 2018 Last revised: 13 Jun 2019

See all articles by Xiyu Jiao

Xiyu Jiao

University of Oxford - Department of Economics

Felix Pretis

University of Victoria, Department of Economics; University of Oxford - Institute for New Economic Thinking at the Oxford Martin School

Date Written: July 20, 2018

Abstract

Algorithms used to detect outliers in regression models have a positive probability of finding outliers even when the data generation process has no outliers. Deriving distributional results on the rate of falsely-discovered outliers, we propose two sets of tests for the overall presence of outliers. First, tests on whether the proportion of detected outliers differs from its expected value. Second, `scaling' tests on whether the proportion of detected outliers decreases proportionally with the level of significance used for detection. We apply the tests to a model of economic growth and a difference-in-differences panel assessing the effectiveness of a carbon tax. The tests are valid for stationary as well as (stochastically) trending regressors and can readily be implemented using Autometrics in PcGive or the R-package gets.

Keywords: misspecification, outlier detection, robust estimation, iterated 1-step Huber-skip M-estimator, indicator saturation

JEL Classification: C12, C52

Suggested Citation

Jiao, Xiyu and Pretis, Felix, Testing the Presence of Outliers in Regression Models (July 20, 2018). Available at SSRN: https://ssrn.com/abstract=3217213 or http://dx.doi.org/10.2139/ssrn.3217213

Xiyu Jiao

University of Oxford - Department of Economics ( email )

10 Manor Rd
Oxford, OX1 3UQ
United Kingdom

Felix Pretis (Contact Author)

University of Victoria, Department of Economics ( email )

3800 Finnerty Rd
Victoria, British Columbia V8P 5C2
Canada

University of Oxford - Institute for New Economic Thinking at the Oxford Martin School ( email )

Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

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