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Forecasting with Leading Economic Indicators—A Neural Network Approach

Timotej Jagric
Business Economics
Vol. 38, No. 4 (October 2003), pp. 42-54
Stable URL: http://www.jstor.org/stable/23490097
Page Count: 13
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Abstract

Despite its obvious importance, short-run prediction of business cycles continues to be a difficult task with limited success in economic analysis. Recent developments, however, suggest that there is scope for adding extensions to the methodology of forecasting major economic fluctuations. In this paper, the author tries to develop a new model that would outperform the forecast accuracy of the classical National Bureau of Economic Research (NBER) leading indicators model. The use of artificial neural networks is proposed here. The main findings are that, at the twelve-month forecasting horizon, improved forecast accuracy could be achieved for in- and out-of-sample data.

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