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Forecasting with leading indicators revisited

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  • Chung-Shu Wu

    (Institute of Economics, Academia Sinica, Taiwan)

  • Ruey S. Tsay

    (Graduate School of Business, University of Chicago, USA)

Abstract

Transfer function or distributed lag models are commonly used in forecasting. The stability of a constant-coefficient transfer function model, however, may become an issue for many economic variables due in part to the recent advance in technology and improvement in efficiency in data collection and processing. In this paper, we propose a simple functional-coefficient transfer function model that can accommodate the changing environment. A likelihood ratio statistic is used to test the stability of a traditional transfer function model. We investigate the performance of the test statistic in the finite sample case via simulation. Using some well-known examples, we demonstrate clearly that the proposed functional-coefficient model can substantially improve the accuracy of out-of-sample forecasts. In particular, our simple modification results in a 25% reduction in the mean squared errors of out-of-sample one-step-ahead forecasts for the gas-furnace data of Box and Jenkins. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Chung-Shu Wu & Ruey S. Tsay, 2003. "Forecasting with leading indicators revisited," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 603-617.
    • Handle: RePEc:jof:jforec:v:22:y:2003:i:8:p:603-617
    DOI: 10.1002/for.879
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    References listed on IDEAS

    as
    1. M. B. Priestley, 1980. "State‐Dependent Models: A General Approach To Non‐Linear Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 47-71, January.
    2. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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    2. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    3. Garcia-Ferrer, A. & de Juan, A. & Poncela, P., 2006. "Forecasting traffic accidents using disaggregated data," International Journal of Forecasting, Elsevier, vol. 22(2), pages 203-222.

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