A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks

Review of Economics and Statistics, Vol. 79, No. 4, November 1997

Posted: 22 Mar 1998

See all articles by Norman R. Swanson

Norman R. Swanson

Rutgers University - Department of Economics

Halbert L. White, Jr.

University of California, San Diego (UCSD) - Department of Economics

Abstract

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 non-adaptive univariate models, (2) non-adaptive 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.

JEL Classification: C53, E37

Suggested Citation

Swanson, Norman Rasmus and White, Halbert L., A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks. Review of Economics and Statistics, Vol. 79, No. 4, November 1997. Available at SSRN: https://ssrn.com/abstract=69448

Norman Rasmus Swanson

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Halbert L. White (Contact Author)

University of California, San Diego (UCSD) - Department of Economics ( email )

9500 Gilman Drive
La Jolla, CA 92093-0508
United States
858-534-3502 (Phone)
858-534-7040 (Fax)

HOME PAGE: http://www.econ.ucsd.edu/~mbacci/white/

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