Structural change and the combination of forecasts

Francis X. Diebold · Peter Pauly

235 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Statistical and numerical algorithms Grey System Theory Applications

Abstract

Abstract Forecasters are generally concerned about the properties of model‐based predictions in the presence of structural change. In this paper, it is argued that forecast errors can under those conditions be greatly reduced through systematic combination of forecasts. We propose various extensions of the standard regression‐based theory of forecast combination. Rolling weighted least squares and time‐varying parameter techniques are shown to be useful generalizations of the basic framework. Numerical examples, based on various types of structural change in the constituent forecasts, indicate that the potential reduction in forecast error variance through these methods is very significant. The adaptive nature of these updating procedures greatly enhances the effect of risk‐spreading embodied in standard combination techniques.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1969The Combination of Forecasts 1984Improved methods of combining forecasts 1974Experience with Forecasting Univariate Time Series and the Combination of Forecasts 1966The Typical Spectral Shape of an Economic Variable 1983Averages of Forecasts: Some Empirical Results 1984A unified view of statistical forecasting procedures

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