Abstract
There is variety of important issues associated with the problem of business cycle forecasting, especially regarding forecast methodology and forecast evaluation. Overall, we can say that macroeconomic forecasting has a fairly poor reputation (Granger 1996). Still, even with the recognition that forecasting business cycles is a very difficult task, we find some hopeful signs for future progress. Our research on forecasting has focused on development of new approach in forecasting with classical NBER leading indicators by applying neural networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bhishop CM (1995) Neural Networks for Pattern Recognition. Oxford University Press: New York
Charemza W,Deadman DF (1992) New directions in econometric practice: General to specific modelling, cointegration and vector autoregression. Edward Elgar, Aldeshot
Granger CWJ (1996) Can We Improve the Predictive Quality of Economic Forecasts? Journal of Applied Econometrics II: 455–473
Hagan MT, Demuth HB, Beale MH (1996) Neural Network Design. PWS Publishing, Boston
Hinton GE (1987) Learning translation invariant recognition in massively parallel networks. In: Bakker JW, Nijman AJ, Treleaven PC (eds) Proceedings PARLE Conference on Parallel Architectures and Languages Europe. Springer Verlag, Berlin
Jagric T (2002) Measuring Business Cycles. Eastern European Economics 40: 63–87
Leitch G, Tanner JE (1991) Economic Forecast Evaluation: Profits Versus The Conventional Error Measures. American Economic Review 81: 581–590
Mezard M, Nadal JP (1989) Learning in feedforward layered networks: The tiling algorithm. Journal of Physics A 22: 2191–2203
Stekler HO (1991) Macroeconomic Forecast evaluation techniques. International Journal of Forecasting, 7: 375–384
Stock JR, Watson MW (1989) New Indexes of Coincident and Leading Economic Indicators. In: Blanchard O, Fischer S (eds) NBER Macroeconomics Annual. MIT Press, Cambridge.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jagric, T. (2003). Forecasting with Leading Economic Indicators — A Neural Network Approach. In: Leopold-Wildburger, U., Rendl, F., Wäscher, G. (eds) Operations Research Proceedings 2002. Operations Research Proceedings 2002, vol 2002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55537-4_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-55537-4_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00387-8
Online ISBN: 978-3-642-55537-4
eBook Packages: Springer Book Archive