How far can we forecast? Statistical tests of the predictive content

Jörg Breitung () and Malte Knüppel

Journal of Applied Econometrics, 2021, vol. 36, issue 4, 369-392

Abstract: We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum forecast horizon h∗. The forecast may result from a survey of forecasters or from an estimated parametric model. The first class of tests compares the mean‐squared prediction error of the forecast to the variance of the evaluation sample, whereas the second class of tests compares it with the mean‐squared prediction error of the recursive mean. We show that the forecast comparison may easily be performed by adopting the encompassing principle, which results in simple regression tests with standard asymptotic inference. Our tests are applied to forecasts of macroeconomic key variables from the survey of Consensus Economics. The results suggest that these forecasts are barely informative beyond two to four quarters ahead.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://doi.org/10.1002/jae.2817

Related works:
Working Paper: How far can we forecast? Statistical tests of the predictive content (2018)
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:wly:japmet:v:36:y:2021:i:4:p:369-392

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().