An Overview of Forecasting Facing Breaks
Jennifer Castle (),
Michael Clements and
David Hendry ()
Journal of Business Cycle Research, 2016, vol. 12, issue 1, No 2, 3-23
Abstract Economic forecasting may go badly awry when there are structural breaks, such that the relationships between variables that held in the past are a poor basis for making predictions about the future. We review a body of research that seeks to provide viable strategies for economic forecasting when past relationships can no longer be relied upon. We explain why model mis-specification by itself rarely causes forecast failure, but why structural breaks, especially location shifts, do. That serves to motivate possible approaches to avoiding systematic forecast failure, illustrated by forecasts for UK GDP growth and unemployment over the recent recession.
Keywords: Business cycles; Forecasting; Breaks (search for similar items in EconPapers)
JEL-codes: C51 C22 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s41549-016-0005-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Working Paper: An Overview of Forecasting Facing Breaks (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:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0005-2
Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/41549
Access Statistics for this article
Journal of Business Cycle Research is currently edited by Michael Graff
More articles in Journal of Business Cycle Research from Springer, Centre for International Research on Economic Tendency Surveys (CIRET)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().