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Article: An integrated regression analysis and time series model for construction tender price index forecasting

TitleAn integrated regression analysis and time series model for construction tender price index forecasting
Authors
KeywordsCost Estimate
Integrated Forecasting Model
Regression Analysis
Tender Price Index Forecast
Time Series Modelling
Issue Date2004
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01446193.asp
Citation
Construction Management And Economics, 2004, v. 22 n. 5, p. 483-493 How to Cite?
AbstractClients need to be informed in advance of their likely future financial commitments and cost implications as the design evolves. This requires the estimation of building cost based on historic cost data that is updated by a forecasted Tender Price Index (TPI), with the reliability of the estimates depending significantly on accurate projections being obtained of the TPI for the forthcoming quarters. In practice, the prediction of construction tender price index movement entails a judgemental projection of future market conditions, including inflation. Statistical techniques such as Regression Analysis (RA) and Time Series (TS) modelling provide a powerful means of improving predictive accuracy when used individually. An integrated RA-TS model is developed and its predictive power compared with the individual RA or TS models. The accuracy of the RA-TS model is shown to outperform the individual RA and TS models in both one and two-period forecasts, with the integrated RA-TS model accurately predicting (95% confidence level) one-quarter forecasts for all the 34 holdout periods involved, with only one period not meeting the confidence limit for two-quarter forecasts. © 2004 Taylor and Francis Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/150346
ISSN
2015 SCImago Journal Rankings: 0.967
References

 

DC FieldValueLanguage
dc.contributor.authorNg, STen_US
dc.contributor.authorCheung, SOen_US
dc.contributor.authorSkitmore, Men_US
dc.contributor.authorWong, TCYen_US
dc.date.accessioned2012-06-26T06:03:36Z-
dc.date.available2012-06-26T06:03:36Z-
dc.date.issued2004en_US
dc.identifier.citationConstruction Management And Economics, 2004, v. 22 n. 5, p. 483-493en_US
dc.identifier.issn0144-6193en_US
dc.identifier.urihttp://hdl.handle.net/10722/150346-
dc.description.abstractClients need to be informed in advance of their likely future financial commitments and cost implications as the design evolves. This requires the estimation of building cost based on historic cost data that is updated by a forecasted Tender Price Index (TPI), with the reliability of the estimates depending significantly on accurate projections being obtained of the TPI for the forthcoming quarters. In practice, the prediction of construction tender price index movement entails a judgemental projection of future market conditions, including inflation. Statistical techniques such as Regression Analysis (RA) and Time Series (TS) modelling provide a powerful means of improving predictive accuracy when used individually. An integrated RA-TS model is developed and its predictive power compared with the individual RA or TS models. The accuracy of the RA-TS model is shown to outperform the individual RA and TS models in both one and two-period forecasts, with the integrated RA-TS model accurately predicting (95% confidence level) one-quarter forecasts for all the 34 holdout periods involved, with only one period not meeting the confidence limit for two-quarter forecasts. © 2004 Taylor and Francis Ltd.en_US
dc.languageengen_US
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01446193.aspen_US
dc.relation.ispartofConstruction Management and Economicsen_US
dc.subjectCost Estimateen_US
dc.subjectIntegrated Forecasting Modelen_US
dc.subjectRegression Analysisen_US
dc.subjectTender Price Index Forecasten_US
dc.subjectTime Series Modellingen_US
dc.titleAn integrated regression analysis and time series model for construction tender price index forecastingen_US
dc.typeArticleen_US
dc.identifier.emailNg, ST:tstng@hkucc.hku.hken_US
dc.identifier.authorityNg, ST=rp00158en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1080/0144619042000202799en_US
dc.identifier.scopuseid_2-s2.0-3242740050en_US
dc.identifier.hkuros91792-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-3242740050&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume22en_US
dc.identifier.issue5en_US
dc.identifier.spage483en_US
dc.identifier.epage493en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridNg, ST=7403358853en_US
dc.identifier.scopusauthoridCheung, SO=7202473419en_US
dc.identifier.scopusauthoridSkitmore, M=7003387239en_US
dc.identifier.scopusauthoridWong, TCY=36839498900en_US

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