[1] Lu Yan, Li Junna,. Forecasting of development of the Jiangsu construction industryand its case analysis [J]. Journal of Southeast University (English Edition), 2009, 25 (4): 541-544. [doi:10.3969/j.issn.1003-7985.2009.04.026]

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Forecasting of development of the Jiangsu construction industryand its case analysis()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 4

Page:

541-544

Research Field:

Economy and Management

Publishing date:

2009-12-30

Info

Title:

Forecasting of development of the Jiangsu construction industryand its case analysis

Author(s):

Lu Yan¹;  Li Junna²

¹School of Civil Engineering, Southeast University, Nanjing 210096, China
²Zhengzhou Track Transportation Company Ltd., Zhengzhou 450008, China

Keywords:

construction industry;  development;  forecasting;  principal component analysis

PACS:

F407.9

DOI:

10.3969/j.issn.1003-7985.2009.04.026

Abstract:

In order to grasp the development path of the Jiangsu construction industry, a multivariable linear regression model for forecasting is proposed. Five factors affecting development of the Jiangsu construction industry are chosen as explanatory variables.They are the construction industry’s fixed assets K, the gross domestic product(GDP), real estate added value(REAV), construction industry export(WS)and investment in construction and installation projects(JA). The principal component analysis is used to resolve multicollinearity between them. The construction added value(CAV)is chosen as a dependant variable, and the growth model of the Jiangsu construction industry is established. Statistical data from 1990 to 2008 are used to test the prediction accuracy of the model. The predictive results show that from 2009 to 2012, the average annual growth rate of the Jiangsu construction industry added value will be 17.65% while the GDP growth rate will be 14.16%; the Jiangsu construction industry will grow faster than the GDP in the near future. The construction output of the GDP continues to rise, and its pillar position will be further strengthened.

References:

[1] Mishra S K. Multicollinearity and maximum entropy leuven estimator[J]. Economics Bulletin, 2004, 25(3): 1-11.
[2] Griffiths William E, Hill R Carter, Lim Guay C. Using Eviews for principles of econometrics[M]. New York: Wiley, 2008: 10-150.
[3] Gao Huixuan. Application of multivariate statistics[M]. Beijing: Beijing University Press, 2005: 25-40.(in Chinese)
[4] Dunteman George H. Principal components analysis[M]. London: Sage Publications, 2005: 10-45.
[5] Schökoph B, Smola A, Müller K. Kernel principal component analysis [C]//Advances in Kernel Methods:Support Vector Learning. Cambridge: MIT Press, 1999: 327-352.
[6] Hotelling H. Analysis of a complex of statistical variables into principal components[J]. Journal of Educational Psychology, 1993, 24(3): 417-444.
[7] The Statistics Bureau of Jiangsu Province. Jiangsu statistical year-book [M]. Beijing: China Statistics Press, 1990-2008.(in Chinese)
[8] Field Andy P. Discovering statistics using SPSS[M]. London: Sage Publications, 2005.
[9] Santner T J, Duffy D E. The statistical analysis of discrete data [M]. New York: Springer-Verlag, 1989: 142-194.
[10] Ding C, Helleseth T, Lam K Y. Several classes of binary sequences with three-level autocorrelation [J]. IEEE Trans Information Theory, 1999, 45(7): 2606-2612.
[11] Ding C, Helleseth T, Martinsen H M. New families of binary sequences with optimal three-level autocorrelation[J]. IEEE Trans Information Theory, 2001, 47(1): 428-433.

Memo

Memo:

Biography: Lu Yan(1978—), female, doctor, lecturer, luyanseu@126. com.
Foundation item: The Program of the Construction Department of Jiangsu Province(No.JS2007-17).
Citation: Lu Yan, Li Junna. Forecasting of development of Jiangsu construction industry and its case analysis[J]. Journal of Southeast University(English Edition), 2009, 25(4): 541-544.

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