ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price
ANN (Artificial Neural Network) models and Spline techniques have been applied to economic analysis, to handle economic problems, evaluate portfolio risk and stock performance, and to forecast stock exchange rates and gold prices. These techniques are improving nowadays and continue to serve as powerful predictive tools. In this study, we compare the performance of ANN models and Bayesian Spline models in forecasting economic datasets. We consider the most commonly used ANN models, which are Generalized Regression Neural Networks (GRNN), Multilayer Perceptron (MLP), and Radial Basis Function Neural Networks (RBFNN). We compare these models using BayesX and Statistica software with three important economic datasets: on the exchange rate of Turkish Liras (TL) to Euro, exchange rate of Turkish Liras (TL) to United States Dollars (USD), and Gold Price for Turkey. With these three economic datasets, we made a comparative study of these models, using the criterions MSE and MAPE to evaluate their forecasting performance. The results demonstrate that the penalized spline model performed best amongst the spline techniques and their Bayesian versions. Amongst the ANN models, the MLP model obtained the best performance criterion results.
Volume (Year): 5 (2013)
Issue (Month): 2 (September)
|Contact details of provider:|| Postal: |
Phone: + 90 312 447 51 95
Fax: + 90 312 447 51 95
Web page: http://www.era.org.tr/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ord, Keith & Hibon, Michele & Makridakis, Spyros, 2000. "The M3-Competition1," International Journal of Forecasting, Elsevier, vol. 16(4), pages 433-436.
- Greiner, Alfred & Kauermann, Goran, 2007. "Sustainability of US public debt: Estimating smoothing spline regressions," Economic Modelling, Elsevier, vol. 24(2), pages 350-364, March.
- Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
- Francesco Audrino & Peter Bühlmann, 2007.
"Splines for Financial Volatility,"
University of St. Gallen Department of Economics working paper series 2007
2007-11, Department of Economics, University of St. Gallen.
- Alfred Greiner, 2009. "Estimating penalized spline regressions: theory and application to economics," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1831-1835.
- Sýdýka Baþçý & Asad Zaman & Arzdar Kiracý, 2010. "Variance Estimates and Model Selection," International Econometric Review (IER), Econometric Research Association, vol. 2(2), pages 57-72, September.
- Makridakis, Spyros & Chatfield, Chris & Hibon, Michele & Lawrence, Michael & Mills, Terence & Ord, Keith & Simmons, LeRoy F., 1993. "The M2-competition: A real-time judgmentally based forecasting study," International Journal of Forecasting, Elsevier, vol. 9(1), pages 5-22, April.
- Marx, Brian D. & Eilers, Paul H. C., 1998. "Direct generalized additive modeling with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 28(2), pages 193-209, August.
- Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114.
- Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
- Sidika Basci & Asad Zaman, 1998. "Variance Estimates and Model Selection," Working Papers 9814, Department of Economics, Bilkent University.
When requesting a correction, please mention this item's handle: RePEc:erh:journl:v:5:y:2013:i:2:p:53-69. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (M. F. Cosar)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.