Forecasting Construction Material Prices Using Vector Error Correction Model
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 8
Abstract
This paper presents a vector error correction (VEC) model for use in forecasting the short- and long-term prices of construction materials. This model characterizes the relationship between construction material prices and a set of relevant explanatory variables. The use of VEC models to forecast construction material prices addresses a gap in the existing literature that stems from overlooking the importance of forecasting both the short- and long-term movements of individual construction materials. The proposed model is applied to produce forecast models for asphalt, steel, and cement prices in the United States based on the identified variables that govern the prices of each material. Practitioners can use the proposed model to forecast construction material prices. The derived insights can be used to enhance the chances of project success.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
References
Akinlo, A. E. 2009. “Electricity consumption and economic growth in Nigeria: Evidence from cointegration and co-feature analysis.” J. Policy Model. 31 (5): 681–693. https://doi.org/10.1016/j.jpolmod.2009.03.004.
Akintoye, A., P. Bowen, and C. Hardcastle. 1998. “Macro-economic leading indicators of construction contract prices.” Constr. Manage. Econ. 16 (2): 159–175. https://doi.org/10.1080/014461998372466.
Ashuri, B., and J. Lu. 2010. “Time series analysis of ENR construction cost index.” J. Constr. Eng. Manage. 136 (11): 1227–1237. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000231.
Ashuri, B., S. M. Shahandashti, and J. Lu. 2012. “Empirical tests for identifying leading indicators of ENR construction cost index.” Const. Manage. Econ. 30 (11): 917–927. https://doi.org/10.1080/01446193.2012.728709.
Belloumi, M. 2009. “Energy consumption and GDP in Tunisia: Cointegration and causality analysis.” Energy Policy 37 (7): 2745–2753. https://doi.org/10.1016/j.enpol.2009.03.027.
Blair, A. N., L. M. Lye, and W. Campbell. 1993. “Forecasting construction cost escalation.” Can. J. Civ. Eng. 20 (4): 602–612. https://doi.org/10.1139/l93-077.
Boswijk, H. P., P. H. Franses, and D. van Dijk. 2010. “Cointegration in a historical perspective.” J. Econometrics 158 (1): 156–159. https://doi.org/10.1016/j.jeconom.2010.03.025.
Box, G. E., and G. M. Jenkins. 1976. Time series analysis forecasting and control. San Francisco, CA: Holden-Day.
Breusch, T. S. 1978. “Testing for autocorrelation in dynamic linear models.” Aust. Econ. Papers 17 (31): 334–355. https://doi.org/10.1111/j.1467-8454.1978.tb00635.x.
Breusch, T. S., and A. R. Pagan. 1980. “The Lagrange multiplier test and its applications to model specification in econometrics.” Rev. Econ. Stud. 47 (1): 239–253. https://doi.org/10.2307/2297111.
Bureau of Labor Statistics. 2017a. “Consumer price index database.” Accessed January 10, 2017. http://www.bls.gov/cpi.
Bureau of Labor Statistics. 2017b. “Employment, hours, and earnings from the current employment statistics survey (national)- average hourly earnings of all construction employees.” Accessed January 10, 2017. http://data.bls.gov/cgi-bin/dsrv.
Bureau of Labor Statistics. 2017c. “Producer price index: Asphalt.” Accessed January 10, 2017. http://data.bls.gov/timeseries/wps058?output_view=pct_3mths.
Bureau of Labor Statistics. 2017d. “Producer price index: Iron and steel.” Accessed January 10, 2017. http://data.bls.gov/timeseries/wpu101?data_tool=XGtable.
Chatfield, C. 1998. “Durbin–Watson test.” In Vol. 3 of Encyclopedia of biostatistics, edited by P. Armitage and T. Colton. New York, NY: Wiley.
Cook, S. 2006. Understanding the construction and interpretation of forecast evaluation statistics using computer-based tutorial exercises. Wales, UK: Swansea Univ.
Dickey, D. A., and W. A. Fuller. 1979. “Distribution of the estimators for autoregressive time series with a unit root.” J. Am. Stat. Assoc. 74 (366a): 427–431. https://doi.org/10.1080/01621459.1979.10482531.
Durbin, J., and G. S. Watson. 1950. “Testing for serial correlation in least squares regression: I.” Biometrika 37 (3/4): 409–428. https://doi.org/10.2307/2332391.
Durbin, J., and G. S. Watson. 1951. “Testing for serial correlation in least squares regression. II.” Biometrika 38 (1–2): 159–178. https://doi.org/10.1093/biomet/38.1-2.159.
Engineering News Record. 2017. “Construction cost index history.” Accessed January 12, 2017. http://www.enr.com/economics/historical_indices/construction_cost_index_history?.
Engle, R. F. 1982. “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation.” Econometrica: J. Econometric Soc. 50 (4): 987–1007. https://doi.org/10.2307/1912773.
Engle, R. F., and C. W. Granger. 1987. “Co-integration and error correction: Representation, estimation, and testing.” Econometrica: J. Econometric Soc. 55 (2): 251–276. https://doi.org/10.2307/1913236.
Engle, R. F., and C. W. J. Granger. 1991. Long-run economic relationships: Readings in cointegration. Oxford, UK: Oxford University Press.
Erdal, G., H. Erdal, and K. Esengün. 2008. “The causality between energy consumption and economic growth in Turkey.” Energy Policy 36 (10): 3838–3842. https://doi.org/10.1016/j.enpol.2008.07.012.
Fan, R. Y., S. T. Ng, and J. M. Wong. 2011. “Predicting construction market growth for urban metropolis: An econometric analysis.” Habitat Int. 35 (2): 167–174. https://doi.org/10.1016/j.habitatint.2010.08.002.
Fanchon, P., and J. Wendel. 1992. “Estimating VAR models under non-stationarity and cointegration: Alternative approaches for forecasting cattle prices.” Appl. Econ. 24 (2): 207–217. https://doi.org/10.1080/00036849200000119.
Godarzi, A. A., R. M. Amiri, A. Talaei, and T. Jamasb. 2014. “Predicting oil price movements: A dynamic artificial neural network approach.” Energy Policy 68 (May): 371–382. https://doi.org/10.1016/j.enpol.2013.12.049.
Godfrey, L. G. 1978. “Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables.” Econometrica: J. Econometric Soc. 46 (6): 1293–1301. https://doi.org/10.2307/1913829.
Granger, C. W. 1969. “Investigating causal relations by econometric models and cross-spectral methods.” Econometrica: J. Econometric Soc. 37 (3): 424–438. https://doi.org/10.2307/1912791.
Gujarati, D. N. 1995. Basic econometrics. New York: McGraw-Hill.
Hamilton, J. D. 1994. Time series analysis. Princeton, NJ: Princeton University Press.
Hua, G. B., and T. H. Pin. 2000. “Forecasting construction industry demand, price and productivity in Singapore: The Box-Jenkins approach.” Constr. Manage. Econ. 18 (5): 607–618. https://doi.org/10.1080/014461900407419.
Hwang, S. 2011. “Time series models for forecasting construction costs using time series indexes.” J. Constr. Eng. Manage. 137 (9): 656–662. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000350.
Hwang, S., M. Park, H. Lee, and H. Kim. 2012. “Automated time-series cost forecasting system for construction materials.” J. Constr. Eng. Manage. 138 (11): 1259–1269. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000536.
Hyndman, R. J., and G. Athanasopoulos. 2013. “Forecasting: Principles and practice.” Accessed November 14, 2016. http://otexts.org/fpp/.
Ilbeigi, M., B. Ashuri, and A. Joukar. 2016. “Time-series analysis for forecasting asphalt-cement price.” J. Manage. Eng. 33 (1): 04016030. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000477.
Jiang, H., and C. Liu. 2011. “Forecasting construction demand: A vector error correction model with dummy variables.” Const. Manage. Econ. 29 (9): 969–979. https://doi.org/10.1080/01446193.2011.611522.
Jiang, H., and C. Liu. 2014. “A panel vector error correction approach to forecasting demand in regional construction markets.” Const. Manage. Econ. 32 (12): 1205–1221. https://doi.org/10.1080/01446193.2014.977800.
Jiang, H., Y. Xu, and C. Liu. 2013. “Construction price prediction using vector error correction models.” J. Constr. Eng. Manage. 139 (11): 04013022. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000729.
Jiang, H., Y. Xu, and C. Liu. 2014. “Market effects on forecasting construction prices using vector error correction models.” Int. J. Constr. Manage. 14 (2): 101–112.
Johansen, S. 1988. “Statistical analysis of cointegration vectors.” J. Econ. Dyn. Control 12 (2–3): 231–254. https://doi.org/10.1016/0165-1889(88)90041-3.
Johansen, S. 2000. “Modelling of cointegration in the vector autoregressive model.” Econ. Modell. 17 (3): 359–373. https://doi.org/10.1016/S0264-9993(99)00043-7.
Johansen, S., and K. Juselius. 1990. “Maximum likelihood estimation and inference on cointegration: With applications to the demand for money.” Oxford Bull. Econ. Stat. 52 (2): 169–210. https://doi.org/10.1111/obes.1990.52.issue-2.
Joukar, A., and I. Nahmens. 2015. “Volatility forecast of construction cost index using general autoregressive conditional heteroskedastic method.” J. Constr. Eng. Manage. 142 (1): 04015051. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001020.
Ljung, G. M., and G. E. Box. 1978. “On a measure of lack of fit in time series models.” Biometrika 65 (2): 297–303. https://doi.org/10.1093/biomet/65.2.297.
Lowe, D. J., M. W. Emsley, and A. Harding. 2006. “Predicting construction cost using multiple regression techniques.” J. Constr. Eng. Manage. 132 (7): 750–758. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(750).
MacKinnon, J. G., A. A. Haug, and L. Michelis 1999. “Numerical distribution functions of likelihood ratio tests for cointegration.” J. Appl. Econom. 14 (5): 563–577.
Murray, M. P. 1994. “A drunk and her dog: An illustration of cointegration and error correction.” Am. Statistician 48 (1): 37–39. https://doi.org/10.2307/2685084.
Ng, S. T., S. O. Cheung, M. Skitmore, and T. C. Wong. 2004. “An integrated regression analysis and time series model for construction tender price index forecasting.” Const. Manage. Econ. 22 (5): 483–493. https://doi.org/10.1080/0144619042000202799.
Ng, S. T., S. O. Cheung, R. M. Skitmore, K. C. Lam, and L. Y. Wong. 2000. “Prediction of tender price index directional changes.” Const. Manage. Econ. 18 (7): 843–852. https://doi.org/10.1080/014461900433122.
Phillips, P. C. 1986. “Understanding spurious regressions in econometrics.” J. Econometrics 33 (3): 311–340. https://doi.org/10.1016/0304-4076(86)90001-1.
Ramey, V. 1993. “How important is the credit channel in the transmission of monetary policy?” In Proc., Carnegie-Rochester Conf. Series on Public Policy, 1–45. Amsterdam, Netherlands: North-Holland.
Said, S. E., and D. A. Dickey. 1984. “Testing for unit roots in autoregressive-moving average models of unknown order.” Biometrika 71 (3): 599–607. https://doi.org/10.1093/biomet/71.3.599.
Shahandashti, S. M., and B. Ashuri. 2013. “Forecasting engineering news-record construction cost index using multivariate time series models.” J. Constr. Eng. Manage. 139 (9): 1237–1243. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000689.
Shahandashti, S. M., and B. Ashuri. 2015. “Highway construction cost forecasting using vector error correction models.” J. Manage. Eng. 32 (2): 04015040. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000404.
Shane, J. S., K. R. Molenaar, S. Anderson, and C. Schexnayder. 2009. “Construction project cost escalation factors.” J. Manage. Eng. 25 (4): 221–229. https://doi.org/10.1061/(ASCE)0742-597X(2009)25:4(221).
Smith, J. P., K. Miller, J. Christofferson, and M. Hutchings. 2011. “best practices for dealing with price volatility in Utah’s residential construction market.” Int. J. Constr. Educ. Res. 7 (3): 210–225. https://doi.org/10.1080/15578771.2011.552935.
Swei, O., J. Gregory, and R. Kirchain. 2016. “Probabilistic approach for long-run price projections: Case study of concrete and asphalt.” J. Constr. Eng. Manage. 143 (1): 05016018. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001211.
Touran, A., and R. Lopez. 2006. “Modeling cost escalation in large infrastructure projects.” J. Constr. Eng. Manage. 132 (8): 853–860. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:8(853).
Tsay, R. S. 2005. Analysis of financial time series. Hoboken, NJ: Wiley.
Weidman, J. E., K. R. Miller, J. P. Christofferson, and J. S. Newitt. 2011. “Best practices for dealing with price volatility in commercial construction.” Int. J. Constr. Educ. Res. 7 (4): 276–293. https://doi.org/10.1080/15578771.2011.552936.
White, H. 1980. “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.” Econometrica: J. Econometric Soc. 48 (4): 817–838. https://doi.org/10.2307/1912934.
Wilmot, C., and G. Cheng. 2003. “Estimating future highway construction costs.” J. Constr. Eng. Manage. 129 (3): 272–279. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:3(272).
Wong, J. M., A. P. Chan, and Y. Chiang. 2005. “Time series forecasts of the construction labour market in Hong Kong: The Box-Jenkins approach.” Const. Manage. Econ. 23 (9): 979–991. https://doi.org/10.1080/01446190500204911.
Wong, J. M., and S. T. Ng. 2010. “Forecasting construction tender price index in Hong Kong using vector error correction model.” Const. Manage. Econ. 28 (12): 1255–1268. https://doi.org/10.1080/01446193.2010.487536.
Wooldridge, J. M. 2009. Introductory econometrics: A modern approach. Mason, OH: South-Western Cengage Learning.
Xu, J.-W., and S. Moon. 2013. “Stochastic forecast of construction cost index using a cointegrated vector autoregression model.” J. Manage. Eng. 29(1): 10–18. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000112.
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Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 8 • August 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Oct 26, 2017
Accepted: Feb 27, 2018
Published online: Jun 9, 2018
Published in print: Aug 1, 2018
Discussion open until: Nov 9, 2018
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