## Abstract Drought forecasting is a critical component of drought risk management. The paper describes an approach to drought forecasting, which makes use of Artificial Neural Network (ANN) and predicts quantitative values of drought indices—continuous functions of rainfall which measure the degre
Forecasting some low-predictability time series using diffusion indices
✍ Scribed by Marc Brisson; Bryan Campbell; John W. Galbraith
- Publisher
- John Wiley and Sons
- Year
- 2003
- Tongue
- English
- Weight
- 254 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0277-6693
- DOI
- 10.1002/for.872
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✦ Synopsis
Abstract
The growth rates of real output and real investment are two macroeconomic time series which are particularly difficult to forecast. This paper considers the application of diffusion index forecasting models to this problem. We begin by characterizing the performance of standard forecasts, via recently‐introduced measures of predictability and the forecast content, noting the maximum horizon at which the forecasts have value. We then compare diffusion index forecasts with a variety of alternatives, including the forecasts made by the OECD. We find gains in forecast accuracy at short horizons from the diffusion index models, but do not find evidence that the maximum horizon for forecasts can be extended in this way. Copyright © 2003 John Wiley & Sons, Ltd.
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