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A Nonlinear Approach to Forecasting with Leading Economic Indicators
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Timotej Jagric
Published/Copyright:
July 1, 2003
The classical NBER leading indicators model was built solely within a linear framework. With recent developments in nonlinear time-series analysis, several authors have begun to examine the forecasting properties of nonlinear models in the field of forecasting business cycles. The research presented in this paper focuses on the development of a new approach to forecasting with leading indicators based on neural networks. Empirical results are presented for forecasting the Index of Industrial Production. The results demonstrate that a superior performance can be obtained relative to the classical model.
Published Online: 2003-7-1
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Stochastic Growth with Increasing Returns: Stability and Path Dependence
- Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses
- Globally-Stabilizing Fiscal Policy Rules
- A Nonlinear Approach to Forecasting with Leading Economic Indicators
- Erratum
- Algorithm
- Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series
