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Forecasting construction demand: a vector error correction model with dummy variables


  • Heng Jiang
  • Chunlu Liu


Modelling the level of demand for construction is vital in policy formulation and implementation as the construction industry plays an important role in a country’s economic development process. In construction economics, research efforts on construction demand modelling and forecasting are various, but few researchers have considered the impact of global economy events in construction demand modelling. An advanced multivariate modelling technique, namely the vector error correction (VEC) model with dummy variables, was adopted to predict demand in the Australian construction market. The results of prediction accuracy tests suggest that the general VEC model and the VEC model with dummy variables are both acceptable for forecasting construction economic indicators. However, the VEC model that considers external impacts achieves higher prediction accuracy than the general VEC model. The model estimates indicate that the growth in population, changes in national income, fluctuations in interest rates and changes in householder expenditure all play significant roles when explaining variations in construction demand. The VEC model with disturbances developed can serve as an experimentation using an advanced econometrical method which can be used to analyse the effect of specific events or factors on the construction market growth.

Suggested Citation

  • Heng Jiang & Chunlu Liu, 2011. "Forecasting construction demand: a vector error correction model with dummy variables," Construction Management and Economics, Taylor & Francis Journals, vol. 29(9), pages 969-979, August.
  • Handle: RePEc:taf:conmgt:v:29:y:2011:i:9:p:969-979
    DOI: 10.1080/01446193.2011.611522

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    References listed on IDEAS

    1. Engle, R. F. & Granger, C. W. J. (ed.), 1991. "Long-Run Economic Relationships: Readings in Cointegration," OUP Catalogue, Oxford University Press, number 9780198283393.
    2. Yap, Ghialy & Allen, David, 2011. "Investigating other leading indicators influencing Australian domestic tourism demand," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1365-1374.
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