Predicting Relative Forecasting Performance : an Empirical Investigation
Granziera, Eleonora; Sekhposyan, Tatevik (15.10.2019)
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JulkaisusarjaInternational Journal of Forecasting
Numero4 ; October-December
Julkaisun pysyvä osoite onhttps://urn.fi/URN:NBN:fi:bof-201906111231
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.
Published in Bank of Finland Research Discussion Papers 23/2018.