Evaluating multi-step system forecasts with relatively few forecast-error observations
David Hendry () and
Andrew Martinez ()
International Journal of Forecasting, 2017, vol. 33, issue 2, 359-372
This paper develops a new approach for evaluating multi-step system forecasts with relatively few forecast-error observations. It extends the work of Clements and Hendry (1993) by using that of Abadir et al. (2014) to generate “design-free” estimates of the general matrix of the forecast-error second-moment when there are relatively few forecast-error observations. Simulations show that the usefulness of alternative methods deteriorates when their assumptions are violated. The new approach compares well with these methods and provides correct forecast rankings.
Keywords: Invariance; Forecast evaluation; Forecast error; Moment matrices; MSFE; GFESM (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations (2016)
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:33:y:2017:i:2:p:359-372
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 ().