Nowadays, it is of vital importance to make predictions about the future in
terms of planning and strategy formulation. This can be realized by accurate and
realistic analysis of information and data that have emerged from past to present.
Especially, governments must make as possible as accurate and realistic prediction
in order to produce an accurate planning and budget based on historical data.
Public expenditure forecasting is an important factor for balance of budget. In
addition, with its multiplier effect, public expenditure has distinctive role on other
components of economy such as national income, employment and private consumption expenditures. That is, public expenditure and forecasting it accurately
have vital importance on the economy of countries. Different approaches namely
stochastic and non-stochastic approaches have been proposed in the literature for
the analysis of time series like this. Particularly, in recent years, the use of non-stochastic models such as fuzzy time series approaches for the analysis of time series
has become widespread. In this study, Expenditures of Central Government Budget (ECGB) of Turkey is forecasted with different fuzzy time series approach. The
fuzzy time series approach is rarely applied for the forecast of public expenditures,
and as far as we know this is the first of such attempts involving Turkish data.
Different fuzzy time series forecasting models are applied to the data data from
January 2007 to May 2013 in order to reach accurate forecasts. Obtained results
from the different fuzzy time series approaches evaluate as a whole. As a result of
the implementation, it is shown that fuzzy time series approaches can be effectively
used to forecast of ECGB
REFERENCES(30)
1.
Schclarek, A. (2007), “Fiscal Policy and Private Consumption in Industrial and Developing Countries”, Journal of Macroeconomics, 29(4), 912-39.
Fatás, A. ve Mihov, I. (2001), “The Effects of Fiscal Policy on Consumption and Employment: Theory and Evidence”, CEPR Discussion Paper, 2760, Centre for Economic Policy Research.
D’Alessandro, A. (2010), “How can Government Spending Affect Private Consumption? A Panel Cointegration Approach”, European Journal of Economics, Finance and Administrative Sciences, 18, 40-57.
Hjelm, G. (2002), “Is Private Consumption Growth Higher (Lower) During Periods of Fiscal Contractions (Expansions)?”, Journal of Macroeconomics, 24(1), 17- 39.
Nieh, C.C. ve Ho, T. (2006), “Does the Expansionary Government Spending Crowd out the Private Consumption? Cointegration Analysis in Panel Data”, The Quarterly Review of Economics and Finance, 46, 133-148.
Gali, J., Salido, L.D.J. ve Valles, J. (2007), “Understanding the Effects of Government Spending on Consumption”, Journal of the European Economic Association, 5(1), 227-270.
Egrioglu, E., Aladag, C.H., Yolcu, U., Uslu, V.R. and Başaran, M.A., 2010. Finding an optimal interval length in high order fuzzy time series, Expert Systems with Applications, 37, 5052-5055.
Egrioglu, E., Aladag, C.H., Başaran, M.A., Uslu, V.R. and Yolcu, U., 2011. A New Approach Based on the Optimization of the Length of Intervals in Fuzzy Time Series, Journal of Intelligent and Fuzzy Systems, 22, 15-19.
Huarng, K. and Yu, T. H. K., 2006a. Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Transactions on Systems, Man,, and Cybernetics-Part B: Cybernetics, 36, 328-340.
Cheng, C. H., Cheng, G. W. and Wang, J. W., 2008. Multi-attribute fuzzy time series method based on fuzzy clustering, Expert Systems with Applications, 34, 1235-1242.
Li, S. T., Cheng, Y. C. and Lin, S. Y., 2008. A FCM-based deterministic forecasting model for fuzzy time series,Computers and Mathematics with Applications, 56, 3052-3063.
Alpaslan, F. and Cagcag, O., 2012. A Seasonal Fuzzy Time Series Forecasting Method Based On Gustafson-Kessel Fuzzy Clustering, Journal of social and Economic Statistics, 2(1), 1-13.
Alpaslan, F., Cagcag, O., Aladag, C. H., Yolcu, U. and Egrioglu, E., 2012. A Novel Seasonal Fuzzy Time Series Method, Hacettepe Journal of Mathematics and Statistics, 43, 375-385.
Egrioglu, E., 2012. A New Time Invariant Fuzzy Time Series Forecasting Method Based On Genetic Algorithm, Advances in Fuzzy Systems, Volume 2012, Article ID 785709, pp.6.
Egrioglu, E., Aladag, C.H. and Yolcu, U., 2013. A Hybrid Fuzzy Time Series Forecasting Model Based on Fuzzy C-Means and Artificial Neural Networks, Expert Systems with Applications, 40, 854-857.
Aladag, C.H., Başaran, M.A., Egrioglu E., Yolcu, U. and Uslu V.R., 2009. Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations, Expert Systems with Applications, 36, 4228-4231.
Yolcu, U., Aladag, C.H., Egrioglu, E. and Uslu, V.R., 2013. Time series forecasting with a novel fuzzy time series approach: an example for İstanbul stock market, Journal of Computational and Statistics Simulation, 83(4), 597-610.
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.