Abstract
The success of decisions depends not only on the behaviors of
decision makers (governments, producers, consumers, and so on) but
also the ability of forecasting future correctly. Forecasting modeling
has a great importance for many research areas as well as economics.
In recent years artificial neural networks (ANNs) have increasingly
been used for forecasting in economics. In this study both ANNs and
vector auto regression method (VAR) are used to solve the exchange
rate model developed for Turkey and the results obtained from the two
methods are compared.
References
- CENTRAL BANK OF THE REPUPLIC OF TURKEY (2007), Statistical Data, www.tcmb.gov.tr.
- GONZALES, S. (2000), “Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models”, Canada Department of Finance Working Papers, 2000-07.
- GRANGER, C. W. J. (1969), “Investigating Casual Relations by Econometric Models andCross-Spectral Methods”, Econometrica, 37, pp. 424-438.
- GREENE, W. H. (1993), Econometric Analysis, Second Edition, Prentice-Hall.
- HAYKIN, S. (1999), Neural Networks: A Comprehensive Foundation, Prentice- Hall.
- KAASTRA, I., and M. BOYD (1996), “Designing A Neural Network For Forecasting Financial and Econometric Time Series”, Neurocomputing, 10 (3), pp. 215-236.
- KOHONEN, T. (1982), “Self-organized Formation of Topologically Correct Feature Maps”, Biological Cybernetics, 43, pp. 59-69.
- MINSKY, M., and S. PAPERT (1969), Perceptrons, MIT Press. SIMS, C. (1980), “Macroeconomics and Reality”, Econometrica, 48, pp. 1-49.
- TURKISH STATISTICAL INSTITUTE (2007), Statistics, www.tuik.gov.tr.
- ZHANG, G., B.E. PATUWO., and M.Y. HU (1998), “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, 14, pp. 35-62.
- ZURADA, J. M. (1992), Introduction of Artificial Neural Systems, West Publishing Company.
Abstract
Verilen kararlar.n ba/ar.l. olmas. yaln.zca karar vericilerin
(hükümetler, üreticiler, tüketiciler, v.b.) davran./lar.na ba2l. olmay.p,
ayn. zamanda gelece2i do2ru biçimde tahmin edebilme yetene2ine de
ba2l.d.r. Tahmin modellemesi birçok ara/t.rma alan. ve ekonomi için
büyük bir öneme sahiptir. Son y.llarda yapay sinir a2lar. (YSA)
ekonomide tahmin amac.yla artan bir biçimde kullan.lmaya
ba/lanm./t.r. Bu çal./mada hem YSA hem de vektör otoregresif metot (VAR) Türkiye için geli/tirilen döviz kuru modelinin çözümünde
kullan.lmakta ve iki metodun sonuçlar. birbirleri ile
kar/.la/t.r.lmaktad.r
Keywords
References
- CENTRAL BANK OF THE REPUPLIC OF TURKEY (2007), Statistical Data, www.tcmb.gov.tr.
- GONZALES, S. (2000), “Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models”, Canada Department of Finance Working Papers, 2000-07.
- GRANGER, C. W. J. (1969), “Investigating Casual Relations by Econometric Models andCross-Spectral Methods”, Econometrica, 37, pp. 424-438.
- GREENE, W. H. (1993), Econometric Analysis, Second Edition, Prentice-Hall.
- HAYKIN, S. (1999), Neural Networks: A Comprehensive Foundation, Prentice- Hall.
- KAASTRA, I., and M. BOYD (1996), “Designing A Neural Network For Forecasting Financial and Econometric Time Series”, Neurocomputing, 10 (3), pp. 215-236.
- KOHONEN, T. (1982), “Self-organized Formation of Topologically Correct Feature Maps”, Biological Cybernetics, 43, pp. 59-69.
- MINSKY, M., and S. PAPERT (1969), Perceptrons, MIT Press. SIMS, C. (1980), “Macroeconomics and Reality”, Econometrica, 48, pp. 1-49.
- TURKISH STATISTICAL INSTITUTE (2007), Statistics, www.tuik.gov.tr.
- ZHANG, G., B.E. PATUWO., and M.Y. HU (1998), “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, 14, pp. 35-62.
- ZURADA, J. M. (1992), Introduction of Artificial Neural Systems, West Publishing Company.
Details
| Primary Language | English |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 1, 2007 |
| Submission Date | September 8, 2015 |
| Published in Issue | Year 2007 Volume: 9 Issue: 1 |