A Models Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks
We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolling window prediction methods indicate that multivariate adaptive linear vector autoregression models often outperform a variety of (1) adaptive and nonadaptive univariate models, (2) nonadaptive multivariate models, (3) adaptive nonlinear models, and (4) professionally available survey predictions. Further, model selection based on the in-sample Schwarz information criterion apparently fails to offer a convenient shortcut to true out-of-sample performance measures. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
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|Date of creation:||1995|
|Date of revision:|
|Contact details of provider:|| Postal: PENNSYLVANIA STATE UNIVERSITY, DEPARTMENT OF ECONOMICS, UNIVERSITY PARK PENNSYLVANIA 16802 U.S.A.|
Web page: http://econ.la.psu.edu/
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