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Time series analysis framework for forecasting the construction labor costs

Faghih, S. A. M ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1007/s12205-021-1489-4
  3. Publisher: Springer Verlag , 2021
  4. Abstract:
  5. This manuscript presents a framework to develop vector error correction (VEC) models applicable to forecasting the short- and long-run movements of the average hourly earnings of construction labor, which is an essential predictor of the construction labor costs. These models characterize the relationship between average hourly earnings and a set of explanatory variables. The framework is applied to develop VEC forecasting models for the average hourly earnings of construction labor in the USA based on the identified variables that govern its movements, such as Global Energy Price Index, Gross Domestic Product, and Personal Consumption Expenditures. More than 150 candidate VEC models were created, of which 25 passed the diagnostics. The most appropriate model was then identified by comparing the prediction performance of these models when applied to the forecasting average hourly earnings over 36-months. The proposed framework and the ensuing models address the need for appropriate models that can forecast the short- and long-run movements of the labor costs. Practitioners can use the proposed framework to develop much-needed forecast models and estimate construction labor costs of the various projects. The insights derived from the development and applications of these models can enhance the chances of project success. © 2021, Korean Society of Civil Engineers
  6. Keywords:
  7. Cost accounting ; Employment ; Error correction ; Forecasting ; Time series analysis ; Wages ; Appropriate models ; Development and applications ; Explanatory variables ; Forecasting models ; Gross domestic products ; Personal consumption ; Prediction performance ; Vector error correction ; Cost benefit analysis
  8. Source: KSCE Journal of Civil Engineering ; Volume 25, Issue 8 , 2021 , Pages 2809-2823 ; 12267988 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s12205-021-1489-4