Time-frequency domain causality of prime building cost and macroeconomic indicators in Ghana: implications for project selection

References

• Abdullah, A.M., Saiti, B., and Masih, M., 2016. The impact of oil price on Islamic stock of South East Asia countries: evidence from MGARCH-DCC and wavelet approaches. Borsa Istanbul review, 16 (4), 219–232.
   Web of Science ®Google Scholar
• Adobor, C.D., 2014. Framework for development of construction cost indices using Unit Rate.MSc Unpublished Thesis Presented to the Department of Building Technology, Kwame Nkrumah University of Science and Technology, Kumasi.
   Google Scholar
• Ahadzie, D.K. and Amoa-Mensah, K., 2010. Management practices in Ghana House Building industry. Journal of science and technology, 30 (2), 62–75.
   Google Scholar
• Akintoye, A., 2010. Analysis of factors influencing project cost estimating practice. Journal construction management and economics, 18 (1), 77–89.
   Google Scholar
• Akintoye, A., Bowen, P., and Hardcastle, C., 1998. Macro-economic leading indicators of construction contract prices. Construction management and economics, 16 (2), 159–175.
   Google Scholar
• Amoa-Mensah, K., 1996. Building estimating manual for West Africa. 3rd ed. Ghana: Parcom Kumasi.
   Google Scholar
• Andrieş, A.M., Ihnatov, I., and Tiwari, A.K., 2016. Co-movement of exchange rates: a wavelet analysis. Emerging markets finance and trade, 52 (3), 574–588.
   Web of Science ®Google Scholar
• Ashuri, B. and Lu, J., 2010. Forecasting ENR construction cost index: a time series analysis approach. Journal of construction engineering and management, 136 (11), 1943–7862.
   Web of Science ®Google Scholar
• Ashworth, A., 2004. Cost studies of buildings. UK: Pearson Education.
   Google Scholar
• Bekiros, S., et al., 2015. On the time scale behavior of equity-commodity links: implications for portfolio management. Journal of international financial markets, institutions & money, 41, 30–46.
   Web of Science ®Google Scholar
• Boako, G. and Alagidede, P., 2016. Co-movement of Africa’s equity markets: regional and global analysis in the frequency–time domains. Physica A: statistical mechanics and its applications, 468, 359–380.
   Web of Science ®Google Scholar
• Breitung, J. and Candelon, B., 2006. Testing for short- and long-run causality: a frequency-domain approach. Journal of econometrics, 132, 363–378.
   Web of Science ®Google Scholar
• Building and Road Research Institute. 2016. The Pozzolana cement technology. Ghana: CSIR.
   Google Scholar
• Clark, T., 2003. Disaggregate evidence on the persistence of consumer price inflation. RWP 03-11. Federal Reserve Bank of Kansas City.
   Google Scholar
• Department of Public Works. 2017. The drivers of the cost of public sector construction; assessment and recommendations. Republic of South Africa: Construction Industry Development Board.
   Google Scholar
• Dröes, M. and van de Minne, A., 2015. Time-varying determinants of long-run house prices. ASRE working paper (2015–08).
   Google Scholar
• Ervin, E., 2007. How to protect profit as material prices rise. Puget sound business journal. Available from: http://seattle.bizjournals.com/seattle/stories/2007/06/11/focus10.html
   Google Scholar
• Ftiti, Z., Guesmi, K., and Abid, I., 2016. Oil price and stock market co-movement: what can we learn from time-scale approaches? International review of financial analysis, 46, 266–280.
   Web of Science ®Google Scholar
• Gallagher, J.P., 2008. Material price escalation: managing the risks. Construction eNewsletter.
   Google Scholar
• Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American statistical association, 77, 304–313.
   Web of Science ®Google Scholar
• Ghana Statistical Service [GSS]. 2013. Ghana living standards survey 6 with labour force module 2012/13. Accra: GSS.
   Google Scholar
• Ghana Statistical Service [GSS]. 2016. Provisional 2016 annual gross domestic product. Accra: Government of Ghana.
   Google Scholar
• Granger, C.W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424–438.
   Web of Science ®Google Scholar
• Grinsted, A., Moore, J.C., and Jevrejeva, S., 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear processes in geophysics, 11, 561–566.
   Web of Science ®Google Scholar
• Hadri, K. and Makhail, W., 2014. Econometric methods and their applications in finance, macro and related fields. USA: World Scientific Publishing Co, Pte, Ltd.
   Google Scholar
• Ho, P.H.K., 2013. Forecasting tender price index under incomplete information. Journal of the operational research society, 64, 1248–1257.
   Web of Science ®Google Scholar
• Hua, G.B. and Pin, T.H., 2010. Forecasting construction industry demand, price and productivity in Singapore: the Box–Jenkins approach. Construction management and economics, 18 (5), 607–618.
   Google Scholar
• Institute of Statistical, Social and Economics Research [ISSER]. 2013. State of the Ghanaian economy. Legon: University of Ghana.
   Google Scholar
• Ji, C. and Domingo, N. (2014). Critical determinats of construction tendering costs in New Zealand: quantity surveyors’ perspectives. In: Proceedings of the 4th New Zealand Built Environment Research Symposium (NZBERS), Auckland, New Zealand.
   Google Scholar
• Joukar, A. and Nahmens, I., 2015. Volatility forecast of construction cost index using General Autoregressive Conditional Heteroskedastic method. Journal of construction engineering and management, 142 (1), 1943–7862.
   Web of Science ®Google Scholar
• Kissi, E., Adjei-Kumi, T., and Badu, E., 2015. Towards the development of tender price index for effective cost planning in the Ghanaian Construction Industry. International journal of construction engineering and management, 4 (3), 73–79.
   Google Scholar
• Lemmens, A., Croux, C., and Dekimpe, M.G., 2008. Measuring and testing Granger-causality over the spectrum: an application to European production expectation surveys. International journal forecasting, 24 (3), 414–431.
   Web of Science ®Google Scholar
• Li, X.-L., et al., 2015. The co-movement and causality between the U.S. Housing and Stock Markets in the time and frequency domains. International review of economics and finance, 38, 220–233.
   Web of Science ®Google Scholar
• Morris, P. and Wilson, W. F., 2006. Measuring and managing cost escalation. AACE International Transactions, CSC.06.
   Google Scholar
• Ojo, A.E. and Awodele, O.A., 2013. Relationship between domestic debt, macro-economic indices and viability of the construction sector in Nigeria. International journal of economics, finance and management sciences, 1 (6), 266–272.
   Google Scholar
• Pindyck, R.S. and Rotemberg, J.J., 1993. The co-movement of stock prices. The quarterly journal of economics, 108 (4), 1073–1104.
   Web of Science ®Google Scholar
• Roesch, A. and Schmidbauer, R., 2014. WaveletComp: Computational wavelet analysis, Rpackageversion.Available from: http://www.hsstat.com/projects/WaveletComp/WaveletComp_guided_tour.pdf.
   Google Scholar
• Rua, A. and Nunes, L.C., 2009. International co-movement of stock market returns: a wavelet analysis. Journal of empirical finance, 16 (4), 632–639.
   Web of Science ®Google Scholar
• Shahandashti, S. M., 2014. Analysis of construction cost variations using macroeconomic, energy and construction market variables. PhD Dissertation (August 2014), Georgia Institute of Technology.
   Google Scholar
• Shahandashti, S.M. and Ashuri, B., 2016. Highway construction cost forecasting using Vector Error Correction models. Journal of management in engineering, 32 (2), 1943–5479.
   Web of Science ®Google Scholar
• Torrence, C. and Compo, G.P., 1998. A practical guide to wavelet analysis. Bulletin of the American meteorological society, 79, 605–618.
   Web of Science ®Google Scholar
• Torrence, C. and Webster, P., 1999. Interdecadal changes in the ESNOM on soon system. Journal of climate, 12, 2679–2690.
   Web of Science ®Google Scholar
• Touran, A. and Lopez, R., 2006. Modeling cost escalation in large infrastructure projects. Journal of construction engineering and management, 132 (8), 853–860
   Web of Science ®Google Scholar
• Williams, T.P., 1994. Predicting changes in construction cost indexes using neural, networks. Journal of construction engineering and management, 120 (2), 306–320.
   Web of Science ®Google Scholar
• Windapo, A., et al., 2017. Determinants of building construction costs in South Africa. Journal of construction business and management, 1 (1), 7–113.
   Google Scholar
• Wong, J.M. and Ng, S.T., 2010. Forecasting construction tenderprice index in Hong Kong using vector error correction model. Construction management and economics, 28 (12), 1255–1268.
   Google Scholar
• Xu, J.W. and Moon, S., 2013. Stochastic forecast of construction cost index using a cointegrated Vector Autoregression model. Journal of management in engineering, 29 (1), 1943–5479.
   Web of Science ®Google Scholar