Forecast Rationality Tests Based on Multi-Horizon Bounds

Andrew Patton and Allan Timmermann

Journal of Business & Economic Statistics, 2012, vol. 30, issue 1, 1-17

Abstract: Forecast rationality under squared error loss implies various bounds on second moments of the data across forecast horizons. For example, the mean squared forecast error should be increasing in the horizon, and the mean squared forecast should be decreasing in the horizon. We propose rationality tests based on these restrictions, including new ones that can be conducted without data on the target variable, and implement them via tests of inequality constraints in a regression framework. A new test of optimal forecast revision based on a regression of the target variable on the long-horizon forecast and the sequence of interim forecast revisions is also proposed. The size and power of the new tests are compared with those of extant tests through Monte Carlo simulations. An empirical application to the Federal Reserve's Greenbook forecasts is presented.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (77)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2012.634337 (text/html)
Access to full text is restricted to subscribers.

Related works:
Journal Article: Forecast Rationality Tests Based on Multi-Horizon Bounds (2011)
Working Paper: Forecast Rationality Tests Based on Multi-Horizon Bounds (2011)
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:taf:jnlbes:v:30:y:2012:i:1:p:1-17

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2012.634337

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().