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Forecast evaluation tests and negative long-run variance estimates in small samples

David Harvey, Stephen Leybourne () and Emily Whitehouse

Discussion Papers from University of Nottingham, Granger Centre for Time Series Econometrics

Abstract: In this paper, we show that when computing standard Diebold-Mariano-type tests for equal forecast accuracy and forecast encompassing, the long-run variance can frequently be negative when dealing with multi-step-ahead predictions in small, but empirically relevant, sample sizes. We subsequently consider a number of alternative approaches to dealing with this problem, including direct inference in the problem cases and use of long-run variance estimators that guarantee positivity. The finite sample size and power of the different approaches are evaluated using extensive Monte Carlo simulation exercises. Overall, for multi-step-ahead forecasts, we find that the recently proposed Coroneo and Iacone (2016) test, which is based on a weighted periodogram long-run variance estimator, offers the best finite sample size and power performance.

Keywords: Forecast evaluation; Long-run variance estimation; Simulation; Diebold-Mariano test; Forecasting (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Date: 2017-03
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