Combination of Forecast Methods Using Encompassing Tests. An Algorithm-Based Procedure ; For the revised version of this paper, see Working Paper 240, Economics Series, June 2009, which includes some changes. The most important change regards the reference of Kisinbay (2007), which was not reported in the previous version. The hierarchical procedure proposed in the paper is based on the approach of Kisinbay (2007), but some modifications of that approach are provided
AbstractThis paper proposes a strategy to increase the efficiency of forecast combining methods. Given the availability of a wide range of forecasting models for the same variable of interest, our goal is to apply combining methods to a restricted set of models. To this aim, an algorithm procedure based on a widely used encompassing test (Harvey, Leybourne, Newbold, 1998) is developed. First, forecasting models are ranked according to a measure of predictive accuracy (RMSFE) and, in a consecutive step, each prediction is chosen for combining only if it is not encompassed by the competing models. To assess the robustness of this procedure, an empirical application to Italian monthly industrial production using ISAE short-term forecasting models is provided.
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Bibliographic InfoPaper provided by Institute for Advanced Studies in its series Economics Series with number 228.
Length: 15 pages
Date of creation: Nov 2008
Date of revision:
Postal: Institute for Advanced Studies - Library, Stumpergasse 56, A-1060 Vienna, Austria
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-11-11 (All new papers)
- NEP-ECM-2008-11-11 (Econometrics)
- NEP-ETS-2008-11-11 (Econometric Time Series)
- NEP-FOR-2008-11-11 (Forecasting)
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- Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Society for Computational Economics, vol. 34(2), pages 173-193, September.
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