Out-of-Sample Inference with Annual Benchmark Revisions

Silvia Goncalves, Michael McCracken and Yongxu Yao ()
Additional contact information
Yongxu Yao: https://www.mcgill.ca/economics/node/42/target%3D

No 2025-020, Working Papers from Federal Reserve Bank of St. Louis

Abstract: This paper examines the properties of out-of-sample predictability tests evaluated with real-time data subject to annual benchmark revisions. The presence of both regular and annual revisions can create time heterogeneity in the moments of the real-time forecast evaluation function, which is not compatible with the standard covariance stationarity assumption used to derive the asymptotic theory of these tests. To cover both regular and annual revisions, we replace this standard assumption with a periodic covariance stationarity assumption that allows for periodic patterns of time heterogeneity. Despite the lack of stationarity, we show that the Clark and McCracken (2009) test statistic is robust to the presence of annual benchmark revisions. A similar robustness property is shared by the bootstrap test of Goncalves, McCracken, and Yao (2025). Monte Carlo experiments indicate that both tests provide satisfactory finite sample size and power properties even in modest sample sizes. We conclude with an application to U.S. employment forecasting in the presence of real-time data.

Keywords: real-time data; bootstrap; prediction; forecast evaluation (search for similar items in EconPapers)
JEL-codes: C12 C52 C53 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2025-09-11
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.20955/wp.2025.020 Full text (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:fip:fedlwp:101742

Ordering information: This working paper can be ordered from

DOI: 10.20955/wp.2025.020

Access Statistics for this paper

More papers in Working Papers from Federal Reserve Bank of St. Louis Contact information at EDIRC.
Bibliographic data for series maintained by Scott St. Louis ().