This paper proposes a strategy to increase the efficiency of forecast combination. Given the availability of a wide range of forecasts for the same variable of interest, our goal is to apply combining methods to a restricted set of models.With this aim, a hierarchical procedure based on an encompassing test is considered. First, forecasting models are ranked according to a measure of predictive accuracy (RMSFE). The models are then selected for combination such that each forecast is not encompassed by any of the competing forecasts. Thus the hierarchical procedure represents a compromise between model selection and model averaging. The robustness of the procedure is investigated in terms of the relative RMSFE using ISAE (Institute for Studies and Economic Analyses) short-term forecasting models for monthly industrial production in Italy.

A hierarchical procedure for the combination of forecasts

COSTANTINI M;
2010-01-01

Abstract

This paper proposes a strategy to increase the efficiency of forecast combination. Given the availability of a wide range of forecasts for the same variable of interest, our goal is to apply combining methods to a restricted set of models.With this aim, a hierarchical procedure based on an encompassing test is considered. First, forecasting models are ranked according to a measure of predictive accuracy (RMSFE). The models are then selected for combination such that each forecast is not encompassed by any of the competing forecasts. Thus the hierarchical procedure represents a compromise between model selection and model averaging. The robustness of the procedure is investigated in terms of the relative RMSFE using ISAE (Institute for Studies and Economic Analyses) short-term forecasting models for monthly industrial production in Italy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/135931
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