Combination of forecast methods using encompassing tests: An algorithm-based procedure

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 228

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Forecasting Models; Simulation Methods
• Subject: combining forecasts ; econometric models ; evaluating forecasts ; models selection ; time series ; Zeitreihenanalyse ; Prognoseverfahren ; Ökonometrisches Modell ; Modellierung ; Theorie ; Schätzung ; Italien

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Costantini, Mauro ; Pappalardo, Carmine
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/72708
• Last update: 10.03.2025, 11:42 AM CET