Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/25316 
Year of Publication: 
2008
Series/Report no.: 
SFB 649 Discussion Paper No. 2008,073
Publisher: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
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.
Subjects: 
Directional forecasts
directional accuracy
forecast evaluation
testing independence
contingency tables
bootstrap
JEL: 
C32
C52
C53
E17
E27
E47
F17
F37
F47
G11
G17
Document Type: 
Working Paper

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