Factor-based forecasting in the presence of outliers: Are factors better selected and estimated by the median than by the mean?

Johannes Tang Kristensen

doi:10.1515/snde-2012-0049

Factor-based forecasting in the presence of outliers

Authors:

Kristensen, Johannes Tang
Close
0000-0002-9494-6619
Department of Economics and Business Economics, Aarhus BSS, Aarhus University

Subtitle:

Are factors better selected and estimated by the median than by the mean?

DOI:

10.1515/snde-2012-0049

Abstract:

Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic with many both theoretical and applied contributions in the literature. In this paper we attempt to address an often neglected issue in these models: The problem of outliers in the data. Most papers take an ad-hoc approach to this problem and simply screen datasets prior to estimation and remove anomalous observations. We investigate whether forecasting performance can be improved by using the original unscreened dataset and replacing principal components with a robust alternative. We propose to use an estimator based on least absolute deviations (LAD) as this alternative and establish a tractable method for computing the estimator. In addition to this we demonstrate the robustness features of the estimator through a number of Monte Carlo simulation studies. Finally, we apply the estimator in a simulated real-time forecasting exercise to test its merits. We use a newly compiled dataset of US macroeconomic series spanning the period 1971:2–2012:10. Our findings suggest that the chosen treatment of outliers does affect forecasting performance and that in many cases improvements can be made using a robust estimator such as the proposed LAD estimator.

Type:

Journal article

Language:

English

Published in:

Studies in Nonlinear Dynamics and Econometrics (online), 2014, Vol 18, Issue 3, p. 309-338

Keywords:

Factors models; Forecasting; Least absolute deviations; Principal components analysis; Robust esitmation

Main Research Area:

Social science

Publication Status:

Published

Review type:

Peer Review

Submission year:

2014

Scientific Level:

Scientific

ID:

262455442

Factor-based forecasting in the presence of outliers

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