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Evaluating the forecast densities of linear and non‐linear models: applications to output growth and unemployment
Author(s) -
Clements Michael P.,
Smith Jeremy
Publication year - 2000
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 - 10.1002/1099-131x(200007)19:4<255::aid-for773>3.0.co;2-g
Subject(s) - econometrics , unemployment , variance (accounting) , statistics , mean squared error , forecast error , unemployment rate , economics , mathematics , macroeconomics , accounting
In economics density forecasts are rarely available, and as a result attention has traditionally focused on point forecasts of the mean and the use of mean square error statistics to represent the loss function. In this paper we apply recently developed methods of forecast density evaluation to compare model‐based density forecasts of US output growth and changes in the unemployment rate. Since one of the models is non‐linear and characterized by a changing error variance, density evaluation may offer greater discrimination than evaluation based on the first moment. Copyright © 2000 John Wiley & Sons, Ltd.