Monte Carlo forecast evaluation with persistent data
Lynda Khalaf and
Charles J. Saunders
International Journal of Forecasting, 2017, vol. 33, issue 1, 1-10
Persistent processes, including local-to-unity and random walks, are commonly considered as forecasting models of interest. However, the associated forecast errors follow non-standard distributions that complicate forecast evaluation tests. We propose a finite sample simulation-based solution to this problem. The method requires a flexible parametric null model that can be simulated as long as a finite dimension nuisance parameter can be specified. The size control of our method is robust to non-standard limiting distributions, such as degenerate asymptotic distribution problems that arise from nested and unit root models. Our simulation studies demonstrate that many of the existing forecast evaluation methods, including various bootstraps, over-reject for highly persistent data. In contrast, our method is level correct and has good power. We extend our approach to the inversion of forecast evaluation statistics in order to construct exact confidence sets for the benchmark model. Confidence sets provide much more information than tests, particularly in the case of the persistence-adjusted relevance of predictive regressors (Rossi, 2005).
Keywords: Evaluating forecasts; Model selection; Stationarity; Statistical tests; Monte Carlo; Finite sample tests (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
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:1:p:1-10
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
Series data maintained by Dana Niculescu ().