Department of Economics and Business Economics

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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.
Original languageEnglish
JournalStudies in Nonlinear Dynamics and Econometrics (Online)
Pages (from-to)309-338
Number of pages30
Publication statusPublished - May 2014

Bibliographical note

Campus adgang til artiklen / Campus access to the article

    Research areas

  • Factors models, Forecasting, Least absolute deviations, Principal components analysis, Robust esitmation

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