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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

Author

Listed:
  • Costantini, Mauro

    (Department of Economics, University of Vienna BWZ, Vienna, Austria)

  • Pappalardo, Carmine

    (Institute for Studies and Economic Analysis (ISAE), Rome, Italy)

Abstract

This 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.

Suggested Citation

  • Costantini, Mauro & Pappalardo, Carmine, 2008. "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 ," Economics Series 228, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:228
    as

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    File URL: http://www.ihs.ac.at/publications/eco/es-228.pdf
    File Function: First version, 2008
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    References listed on IDEAS

    as
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    Cited by:

    1. Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 34(2), pages 173-193, September.

    More about this item

    Keywords

    Combining forecasts; Econometric models; Evaluating forecasts; Models selection; Time series;

    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; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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