An econometric model for forecasting private construction investment in Hong Kong

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

15 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)519-534
Journal / PublicationConstruction Management and Economics
Volume29
Issue number5
Publication statusPublished - May 2011
Externally publishedYes

Abstract

Acknowledging the importance of the private construction market and a close linkage between private construction investment, public sector output and general economic conditions, there is a strong motivation to develop reliable models to forecast private construction investment. Based on the Hong Kong scenario, two modelling approaches, namely the vector error correction (VEC) and the multiple regression models are developed and compared for their modelling accuracy and ability to handle non-stationary time series data. The result suggests that private construction investment in Hong Kong can be predicted by reference to public investment in construction, gross domestic product (GDP) and unemployment rate. All in all, the VEC model is considered more accurate and robust in handling non-stationary data. Through the VEC model, it is possible to confirm that the crowding-in effect of public work programmes, though minimal, is discernible in private construction investment in Hong Kong. Yet private construction investment is more sensitive to general economic conditions, as represented by GDP and unemployment rate. The GDP could represent the ability of investors to pay for construction items, while the unemployment rate is used as a proxy for the willingness of end-users to purchase the construction items. The models proposed should help policy and decision makers formulate suitable policies and strategies to sustain the construction industry in the medium to long run. © 2011 Taylor & Francis.

Research Area(s)

  • Crowding-in effect, Private construction investment, Regression analysis, Stationarity, Vector error correction model

Bibliographic Note

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