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Testing directional forecast value in the presence of serial correlation

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  • Oliver Blaskowitz
  • Helmut Herwartz

Abstract

Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations. Yet, in the presence of serial correlation they are markedly oversized as confirmed in a simulation study. We summarize serial correlation robust test procedures and propose a bootstrap approach. By means of a Monte Carlo study we illustrate the relative merits of the latter. Two empirical applications demonstrate the relevance to account for serial correlation in economic time series when testing for the value of directional forecasts.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-073.

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Length: 37 pages
Date of creation: Dec 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-073

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Keywords: Directional forecasts; directional accuracy; forecast evaluation; testing independence; contingency tables; bootstrap;

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Cited by:
  1. Tsuchiya, Yoichi, 2014. "Purchasing and supply managers provide early clues on the direction of the US economy: An application of a new market-timing test," International Review of Economics & Finance, Elsevier, Elsevier, vol. 29(C), pages 599-618.

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