Abstract
In this paper we first give a new method of time series model to forecast GDP of China. The method proposed here aims to emphasis the importance of the impact of STEP-Affair on the GDP forecasting. The superiority of the method to ARMA model is illustrated in an example presented accordingly. Then in the system of whole economic when the GDP forecasted above is given, how can we allocate the limited resources to make the economical behavior relative efficient. We use data envelopment analysis to show how to determine input interval. Each input among the input interval as well as the given output constitute an efficient decision making unit (DMU). For decision makers the techniques are very important in decision making, especially in macroeconomic policies making in China.
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References
Ming-lei, Z.: Intervention Analysis with Time-Series to the World Crude Oil Price. Mathematics in practice and theory 134, 12–18 (2004)
Jian, W.: Application of Temporal Sequence Analysis Technique in GDP Growth Prediction. Journal of Xiaogan University 25, 55–57 (2005)
Jin-hai, H., Shou-sheng, X.: AR Model - based Prediction of Metal Content in Lubricating Oil. Experimentation and Research on Gas Turbine 16, 32–35 (2003)
Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444 (1978)
Emrouznejad, A., Thanassoulis, E.: An extensive bibliography of data envelopment analysis, Volum I and II. Varwick Business School Research Paper, University of Warwick (1996)
Han, M., Xi, J., Xu, S., Yin, F.-L.: Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Trans. Signal Processing 52, 3409–3416 (2004)
Wang, L.P., Teo, K.K., Lin, Z.: Predicting time series using wavelet packet neural networks. In: Proc. IJCNN 2001, pp. 1593–1597 (2001)
Castillo, O., Melin, P.: Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory. IEEE Trans. Neural Networks 13, 1395–1408 (2002)
Rajapakse, J.C., Wang, L.P. (eds.): Neural Information Processing: Research and Development. Springer, Berlin (2004)
Abrahan, B., Edolter, J.L.: Statistical Methods for Forecasting. Wiley, New York (1983)
Andereon, T.W.: The Statistical Analysis of Time Series. Wiley, New York (1971)
Box, G.E.P., Jekins, G.M., Reinsel, G.C.: Time Series Analysis- Forecasting and control, 3rd edn. Prentice Hall, Englewood Cliffs (1994)
Helson, H., Lowdenslager, D.: Predition theory and Fourier series in several variables. Acta Mathematica 99, 165–202 (1958)
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Yu-quan, C., Li-jie, M., Ya-peng, X. (2006). Forecasting GDP in China and Efficient Input Interval. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_124
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DOI: https://doi.org/10.1007/11881223_124
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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