Forecast rationality tests based on multi-horizon bounds

Published

Journal Article

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. © 2012 American Statistical Association.

Full Text

Duke Authors

Cited Authors

  • Patton, AJ; Timmermann, A

Published Date

  • January 1, 2012

Published In

Volume / Issue

  • 30 / 1

Start / End Page

  • 1 - 17

Electronic International Standard Serial Number (EISSN)

  • 1537-2707

International Standard Serial Number (ISSN)

  • 0735-0015

Digital Object Identifier (DOI)

  • 10.1080/07350015.2012.634337

Citation Source

  • Scopus