Combining Forecasts Based on Multiple Encompassing Tests in a Macroeconomic Core System
Mauro Costantini and
Robert Kunst ()
No 243, Economics Series from Institute for Advanced Studies
We investigate whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not covered by other models. The potential benefits of this procedure are explored in extensive Monte Carlo simulations using realistic designs that are adapted to U.K. and to French macroeconomic data. The real economic growth rates of these two countries serve as the target series to be predicted. Generally, we find that the test-based averaging of forecasts yields a performance that is comparable to a simple uniform weighting of individual models. In one of our role-model economies, test-based averaging achieves some advantages in small samples. In larger samples, pure prediction models outperform forecast averages.
Keywords: Combining forecasts; encompassing tests; model selection; time series (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Pages: 24 pages
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-for
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Journal Article: Combining forecasts based on multiple encompassing tests in a macroeconomic core system (2011)
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