Predicting relative forecasting performance: An empirical investigation
Eleonora Granziera () and
Tatevik Sekhposyan ()
International Journal of Forecasting, 2019, vol. 35, issue 4, 1636-1657
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.
Keywords: Conditional predictive ability; Model selection; Model averaging; Inflation forecasts; Output growth forecasts (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Predicting relative forecasting performance: An empirical investigation (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1636-1657
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().