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Testing for the usefulness of forecasts
Author(s) -
Lin Eric S.,
Chou PingHung,
Chou TaSheng
Publication year - 2011
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1180
Subject(s) - flexibility (engineering) , computer science , bayesian probability , econometrics , test (biology) , survey of professional forecasters , variable (mathematics) , index (typography) , bayesian information criterion , economics , machine learning , artificial intelligence , monetary policy , mathematics , management , macroeconomics , paleontology , mathematical analysis , world wide web , biology
Ashley ( Journal of Forecasting 1983; 2 (3): 211–223) proposes a criterion (known as Ashley's index) to judge whether the external macroeconomic variables are well forecast to serve as explanatory variables in forecasting models, which is crucial for policy makers. In this article, we try to extend Ashley's work by providing three testing procedures, including a ratio‐based test, a difference‐based test, and the Bayesian approach. The Bayesian approach has the advantage of allowing the flexibility of adapting all possible information content within a decision‐making environment such as the change of variable's definition due to the evolving system of national accounts. We demonstrate the proposed methods by applying six macroeconomic forecasts in the Survey of Professional Forecasters. Researchers or practitioners can thus formally test whether the external information is helpful. Copyright © 2010 John Wiley & Sons, Ltd.