IDEAS home Printed from https://ideas.repec.org/p/ihs/ihsesp/228.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: https://irihs.ihs.ac.at/id/eprint/1868
    File Function: First version, 2008
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ihs:ihsesp:228. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Doris Szoncsitz (email available below). General contact details of provider: https://edirc.repec.org/data/deihsat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.