Statistical tests for equal predictive ability across multiple forecasting methods
Daniel Borup and
Martin Thyrsgaard ()
Additional contact information
Martin Thyrsgaard: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Abstract:
We develop a multivariate generalization of the Giacomini-White tests for equal conditional predictive ability. The tests are applicable to a mixture of nested and non-nested models, incorporate estimation uncertainty explicitly, and allow for misspecification of the forecasting model as well as non-stationarity of the data. We introduce two finite-sample corrections, leading to good size and power properties. We also provide a two-step Model Confidence Set-type decision rule for ranking the forecasting methods into sets of indistinguishable conditional predictive ability, particularly suitable in dynamic forecast selection. In the empirical application we consider forecasting of the conditional variance of the S&P500 Index.
Keywords: forecast comparison; multivariate tests of equal predictive ability; Giacomini-White test; Diebold-Mariano test; conditional forecast combination (search for similar items in EconPapers)
JEL-codes: C12 C52 C53 (search for similar items in EconPapers)
Pages: 67
Date: 2017-05-17
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://repec.econ.au.dk/repec/creates/rp/17/rp17_19.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2017-19
Access Statistics for this paper
More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().