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ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price

Listed author(s):
  • Ozer Ozdemir


    (Anadolu University, Faculty of Science, Department of Statistics.)

  • Memmedaga Memmedli


    (Anadolu University, Faculty of Science, Department of Statistics.)

  • Akhlitdin Nizamitdinov


    (Anadolu University, Faculty of Science, Department of Statistics)

Registered author(s):

    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.

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    Article provided by Econometric Research Association in its journal International Econometric Review.

    Volume (Year): 5 (2013)
    Issue (Month): 2 (September)
    Pages: 53-69

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    Handle: RePEc:erh:journl:v:5:y:2013:i:2:p:53-69
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Greiner, Alfred & Kauermann, Goran, 2007. "Sustainability of US public debt: Estimating smoothing spline regressions," Economic Modelling, Elsevier, vol. 24(2), pages 350-364, March.
    8. Alfred Greiner, 2009. "Estimating penalized spline regressions: theory and application to economics," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1831-1835.
    9. Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670.
    10. Ord, Keith & Hibon, Michele & Makridakis, Spyros, 2000. "The M3-Competition1," International Journal of Forecasting, Elsevier, vol. 16(4), pages 433-436.
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