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A Peer-reviewed scientific articles/A1 Journal article (refereed), original research
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Predicting Relative Forecasting Performance: an Empirical Investigation, International Journal of Forecasting (2019). Granziera, Eleonora; Sekhposyan, Tatevik

Category A Peer-reviewed scientific articles
Sub-category A1 Journal article (refereed), original research
auki Internal authors
All authors as text Granziera, Eleonora; Sekhposyan, Tatevik 
Number of authors
Status Online First
Year of publication 2019 
Date 31.05.2019 
Name of article Predicting Relative Forecasting Performance: an Empirical Investigation 
Name of journal International Journal of Forecasting
Abstract The relative performances of forecasting models change over time. This empirical observation raises two questions. First, is the relative performance itself predictable? Second, if so, can it be exploited in order to improve the forecast accuracy? We address these questions by evaluating the predictive abilities of a wide range of economic variables for two key US macroeconomic aggregates, namely industrial production and inflation, relative to simple benchmarks. We find that business cycle indicators, financial conditions, uncertainty and measures of past relative performances are generally useful for explaining the models’ relative forecasting performances. In addition, we conduct a pseudo-real-time forecasting exercise, where we use the information about the conditional performance for model selection and model averaging. The newly proposed strategies deliver sizable improvements over competitive benchmark models and commonly-used combination schemes. The gains are larger when model selection and averaging are based on both financial conditions and past performances measured at the forecast origin date.
Free text descriptor in Finnish ennusteet; mallit; tarkkuus; inflaatio; tuotanto; teollisuustuotanto; 
Free text descriptor in English Conditional predictive ability; Model selection; Model averaging; Inflation forecasts; Output growth forecasts 
ISSN / e-ISSN 0169-2070 
auki Internet addresses
Additional information Available online 31 May 2019
Open Access Not known