Time Series Models for Forecasting: Testing or Combining?
AbstractIn this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select a model, based on which forecasts are made. However, besides the well-known difficulty in dealing with multiple tests, the testing approach has a potentially serious drawback: controlling the probability of Type I error at a conventional level (e.g., 0.05) often excessively favors the null, which can be problematic for the purpose of forecasting. In addition, as shown in this paper, testing procedures can be very unstable, which results in high variability in the forecasts.
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Bibliographic InfoArticle provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.
Volume (Year): 11 (2007)
Issue (Month): 1 (March)
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