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Dissecting Models' Forecasting Performance

Boriss Siliverstovs

No 15-397, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

Abstract: In this paper we suggest an approach to comparison of models' forecasting performance in unstable environments. Our approach is based on combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) suggested earlier in Welch and Goyal (2008) and the Bayesian change point analysis based on Barry and Hartigan (1993). The latter methodology provides the formal statistical analysis of the CSSFED time series which turned out to be a powerful graphical tool for tracking how the relative forecasting performance of competing models evolves over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.

Keywords: Forecasting; Forecast Evaluation; Change Point Detection; Bayesian Estimation (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
Date: 2015-11
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