A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series / James H. Stock, Mark W. Watson.

Format:

Book

Author/Creator:

Stock, James H.

Contributor:

National Bureau of Economic Research.

Watson, Mark W.

Series:

Working Paper Series (National Bureau of Economic Research) no. w6607.

NBER working paper series no. w6607

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1998.

Summary:

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.

Notes:

Print version record

June 1998.