Comparing Simple Forecasting Methods and Complex Methods: A Frame of Forecasting Competition


  • Emrah Gulay



dynamic relation model, ANFIS, ARIMA-ANFIS, gross capital formation, domestic savings


The gross capital formation (GCF), which helps to gradually increase GDP itself, is financed by domestic savings (DS) in both developed and developing countries. Therefore, forecasting GCF is the key subject to the economists’ decisions making. In this study, I use simple forecasting methods, namely dynamic relation model called “Autoregressive Distributed Lag Model (ARDL)”, and complex methods such as Adaptive Neuro Fuzzy Inference System (ANFIS) method and ARIMA-ANFIS method to determine which method provides better out-of-sample forecasting performance. In addition, the contribution of this study is to show how important to use domestic savings in forecasting GCF. On the other hand, ANFIS and hybrid ARIMA-ANFIS methods are comparatively new, and no GCF modeling using ANFIS and ARIMA-ANFIS was attempted until recently to the best of my knowledge. In addition, Autoregressive Integrated Moving Average (ARIMA) method and Vector Autoregressive (VAR) model serve as benchmarks, allowing for fair competing for the study.

JEL Codes - C45; C53


Claycombe, W., and Sullivan, W., 1977. Fundamental of forecasting. Reston, VA: Reston Publishing Company.

Dickey, D. A., and Fuller, W. A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431. doi:

Engle, R. F., and Yoo, B. S., 1987. Forecasting and testing in co-integrated systems. Journal of Econometrics, 35(1), 143-159. doi:

Fanchon, P., and Wendel, J., 1992. Estimating VAR models under non-stationarity and cointegration: Alternative approaches for forecasting cattle prices. Applied Economics, 24(2), 207-217. doi:

Granger, C. W. J., 1981. Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16(1), 121-130. doi:

Hall, A. D., Heather, M. A., and Granger, C. W. J., 1992. A cointegration analysis of treasury bill yields. The Review of Economics and Statistics, 74(1), 116-126. doi:

Hendry, D. F., and Clements, M. P., 2003. Economic forecasting: some lessons from recent research. Economic Modelling, 20(2), 301-329. doi:

Hernandez, C. A. S., Pedraza, L. F. M., and Salcedo, O. J. P., 2010. Comparative analysis of time series techniques ARIMA and ANFIS to forecast wimax traffic. Electron Electric Eng, 2(2), 223–228.

Jang, J. S. R., 1993. ANFIS: Adaptive-network-based fuzzy inference systems. EEE Transactions on Systems. Manand Cybernetics, 23(3), 665-685. doi:

Johansen, S., 1995. Likelihood-Based Inference in Cointegrated Vector Autoregreesive Models. New York: Oxford University press. doi:

Kaur, A., Kamaldeep, K., and Ruchika, M., 2010. Soft computing approaches for prediction of software maintenance effort. International Journal of Computers and Applications, 1(16), 339-515. doi:

Larson, D., 1983. Summary statistics and forecasting performance. Agricultural Economics Research, 35(3), 11-22.

Mallick, H., and Agarwal, S., 2005. Financial Liberalisation and Economic Growth in India: A Long-Run Analysis. www.researchgate.netpublication/252308491_Financial_Liberalisation_and_


Nelson, C. R., 1972. The prediction performance of the FRB-MIT-PENN model of the US economy. The American Economic Review, 62(5), 902-917.

Newbold, P., and Granger, C. W. J., 1974. Experience with forecasting univariate time series and the combination of forecasts. Journal of the

Royal Statistical Society. Series A (General), 137(2), 131-165. doi:

Pesaran, M. H., and Shin, Y., 1999. An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis. In S. Strøm (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371-413). Cambridge: Cambridge University Press. doi:

Phillips, P. C. B., and Perron, P., 1988. Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. doi:

Rahman, M., Islam, A. H. M. S., Nadvi, S. Y. M., and Rahman, R. M., 2013. International Conference on Informatics: Electronics and Vision. doi:

Reid, D. J., 1971. Forecasting in action: a comparison of forecasting techniques in economic time series. Joint Conference of OR Society's Group on Long-Range Planning and Forecasting.

Sabur, S. A., and Haque, M. E., 1993. An analysis of rice price in Mymensing town market: Pattern and forecasting. Bangladesh Journal of Agricultural Economics, 16(2), 61-75.

Sims, C. A., 1980. Macroeconomics and Reality. Econometrica, 48(1), 1-48. doi:

Sugeno, M., and Kang, G., 1988. Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(1), 15-33. doi:

Tektas, M., 2010. Weather forecasting using ANFIS and ARIMA models. A case study for Istanbul. Environmental Research, Engineering and Management, 51(1), 5-10.

Yayar, R., Hekim, M., Y?lmaz, V., and Bakirci, F., 2011. A comparison of ANFIS and ARIMA techniques in the forecasting of electrical energy consumption of Tokat province in Turkey. Journal of Economic and Social Studies, 1(2), 87-112. doi:




How to Cite

Gulay, E. (2018). Comparing Simple Forecasting Methods and Complex Methods: A Frame of Forecasting Competition. Scientific Annals of Economics and Business, 65(2), 159–169.