Research on Management Policy and National Real Estate Climate Index in China

• Autorzy: An H. , Ding Y. , Gao X. , Li J.

Warianty tytułu

PL

Badania nad polityką zarządzania wykorzystującą indeks NRECI – National Real Estate Climate Index

• Języki publikacji: EN

Abstrakty

EN

Using ARIMA time series analysis method, this paper predicts NRECI from May to December 2011. Then detailed analysis is made of the NRECI trend and the major management policies introduced in corresponding periods. The results show that NRECI is closely related with management policy of real estate industry in China. The development trend of the NRECI from May to December 2011 indicates that the authorities should take moderate management measures to keep the stable trend sustained.

PL

W artykule przedstawiono metodę obliczania iprzewidywnia współczynnika NRECI (National Real Estate Climate Index). Do tego celu wykorzystano metodę analizy szeregu ARIMA.

Słowa kluczowe

EN

China industry; ARIMA model; NRECI index

PL

model ARIMA; indeks NRECI; przemysł chiński

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 9b
• Strony: 25--28
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

An H.

• Lab of Resources and Environmental Management, China University of Geosciences (Beijing)
• School of Humanites and Economic Management, China University of Geosciences (Beijing)

autor

Ding Y.

• School of Humanites and Economic Management, China University of Geosciences (Beijing)
• Lab of Resources and Environmental Management, China University of Geosciences (Beijing)

autor

Gao X.

• Lab of Resources and Environmental Management, China University of Geosciences (Beijing)
• School of Humanites and Economic Management, China University of Geosciences (Beijing)

autor

Li J.

• School of Humanites and Economic Management, China University of Geosciences (Beijing)
• Lab of Resources and Environmental Management, China University of Geosciences (Beijing)

Bibliografia

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• [3] T. P. Williams, Predicting changes in construction cost indexes using neural networks, Constr. Eng. Manage., 120(1994), No.2, 306-320
• [4] N. Kulendran, and S. F. Witt, Leading indicator tourism forecasts, Tourism Management, 24(2003), No.5, 503-510
• [5] B. Ashuri, and J. Lu, Time Series Analysis of ENR Construction Cost Index, Journal of Construction Engineering and Management, 136(2010), No.11, 1227-1237
• [6] P. J. Brockwell, and R. A. Davis, Introduction to time series and forecasting, 2nd Ed., Springer, New York (2002)
• [7] G. Box, and G. M. Jenkins, Time series analysis: Forecasting and control, Holden-Day, Oakland, Calif. (1970)
• [8] P. K. Watson, and S. S. Teelucksingh, A practical introduction to econometric methods: Classical and modern, Univ. of the West Indies Press, Kingston, Jamaica (2002)