A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series

James Stock and Mark Watson

No 6607, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: A forecasting comparison is undertaken in which 49 univariate forecasting methods, plus various forecast pooling procedures, are used to forecast 215 U.S. monthly macroeconomic time series at three forecasting horizons over the period 1959 - 1996. All forecasts simulate real time implementation, that is, they are fully recursive. The forecasting methods are based on four classes of models: autoregressions (with and without unit root pretests), exponential smoothing, artificial neural networks, and smooth transition autoregressions. The best overall performance of a single method is achieved by autoregressions with unit root pretests, but this performance can be improved when it is combined with the forecasts from other methods.

JEL-codes: C22 C32 (search for similar items in EconPapers)
Date: 1998-06
New Economics Papers: this item is included in nep-ecm
Note: EFG ME
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Citations: View citations in EconPapers (107)

Published as "Evidence on Structural Instability in Macroeconomic Time Series Relations", JBES, Vol. 14, no. 1 (January 1996): 11-30.

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