Public expenditure forecasting with fuzzy time series

Dilemmas of contemporary public administration

Pobierz cytowanie

ROZDZIAŁ KSIĄŻKI (213-220)

Ufuk Yolcu

Cagdas Hakan Aladag

Więcej

Ukryj

Pobierz (PDF)

Referencje (30)

REFERENCJE (30)

Schclarek, A. (2007), “Fiscal Policy and Private Consumption in Industrial and Developing Countries”, Journal of Macroeconomics, 29(4), 912-39.

Google Scholar

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.

Google Scholar

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.

Google Scholar

Karras, G. (1994), “Government Spending and Private Consumption: Some International Evidence”, Journal of Money, Credit, and Banking, 26(1), 9-22.

Google Scholar

Hjelm, G. (2002), “Is Private Consumption Growth Higher (Lower) During Periods of Fiscal Contractions (Expansions)?”, Journal of Macroeconomics, 24(1), 17- 39.

Google Scholar

Okubo, M. (2003), “Intratemporal Substitution between Private and Government Consumption: The Case of Japan”, Economics Letters, 79(1), 75-81.

Google Scholar

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.

Google Scholar

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.

Google Scholar

Song, Q. and Chissom, B.S., 1993a. Forecasting enrollments with fuzzy time seriesPart I, Fuzzy Sets and Systems, 54, 1-10.

Google Scholar

10.

Song, Q. and Chissom, B.S., 1993b. Fuzzy time series and its models, Fuzzy Sets and Systems, 54, 269-277.

Google Scholar

11.

Song, Q. and Chissom, B.S., 1994. Forecasting enrollments with fuzzy time series- Part II, Fuzzy Sets and Systems, 62 1-8.

Google Scholar

12.

Chen, S. M., 1996. Forecasting enrollments based on fuzzy time-series, Fuzzy Sets and Systems, 81, 311-319.

Google Scholar

13.

Chen, S.M., 2002. Forecasting enrollments based on high order fuzzy time series, Cybernetics and Systems, 33, 1-16.

Google Scholar

14.

Huarng, K., 2001. Effective length of intervals to improve forecasting in fuzzy time-series, Fuzzy Sets and Systems, 123, 387-394.

Google Scholar

15.

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.

Google Scholar

16.

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.

Google Scholar

17.

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.

Google Scholar

18.

Huarng, K. andYu, H. K., 2006b. The application of neural networks to forecast fuzzy.

Google Scholar

19.

time series, Physica A, 363, 481-491.

Google Scholar

20.

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.

Google Scholar

21.

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.

Google Scholar

22.

Aladag, C. H., Yolcu, U., Egrioglu, E. and Dalar A.Z., 2012. A new time invariant fuzzy.

Google Scholar

23.

time series forecasting method based on particle swarm optimization, Applied Soft Computing, 12, 3291-3299.

Google Scholar

24.

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.

Google Scholar

25.

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.

Google Scholar

26.

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.

Google Scholar

27.

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.

Google Scholar

28.

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.

Google Scholar

29.

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.

Google Scholar

30.

Durukaya, M., 2012. The Relationship between Public Expenditure and Private Consumption in Turkey, Maliye Dergisi (Journal of Finance), 163, 1-12.

Google Scholar

Udostępnij

Wyślij mailem

Indeksy

Indeks słów kluczowych

Indeks autorów