Abstract
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.
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Abbreviations
- AAHE:
-
Adjusted Average Hourly Earnings
- ADF:
-
Augmented Dickey-Fuller
- AHE:
-
Average Hourly Earnings of Employees in Construction
- AIC:
-
Akaike Information Criterion
- ARIMA:
-
Autoregressive Integrated Moving Average
- BP:
-
Building Permits
- CCI:
-
Construction Cost Index
- CES:
-
Current Employment Statistics
- CL:
-
Civilian Labor Force
- CPI:
-
Consumer Price Index
- EC:
-
Employees in Construction
- EUS:
-
Employees in the US
- GDP:
-
Gross Domestic Product
- GEP:
-
Global Energy Price Index
- GNI:
-
Gross National Income
- HS:
-
Housing Starts
- I(1):
-
A series with the first order of integration
- IR:
-
Interest Rates
- MAE:
-
Mean Absolute Error
- MAPE:
-
Mean Absolute Percentage Error
- MS:
-
Money Stock
- N=:
-
Total number of periods
- n:
-
Dimension of the vector variable
- NHCCI:
-
National Highway Construction Cost Index
- NS:
-
NASDAQ Composite Index
- OLS:
-
Ordinary Least Square
- PCE:
-
Personal Consumption Expenditures
- PI:
-
Personal Income
- RMSE:
-
Root Mean Squared Error
- SIC:
-
Schwarz Information Criterion
- t:
-
Time
- UL:
-
Unemployment Level
- VAR:
-
Vector Autoregressive
- VEC:
-
Vector Error Correction
- WTI:
-
WTI Oil price
- y a,t :
-
Actual value at time t
- y f,t :
-
Forecasted value at time t
- Yt:
-
Vector of variables
- β :
-
Drift / Intercept
- εt :
-
Residual series
- Δ:
-
Subtract of consecutive values
- Γi :
-
Parameters to estimate
- Π:
-
Cointegration rank
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Faghih, S.A.M., Gholipour, Y. & Kashani, H. Time Series Analysis Framework for Forecasting the Construction Labor Costs. KSCE J Civ Eng 25, 2809–2823 (2021). https://doi.org/10.1007/s12205-021-1489-4
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DOI: https://doi.org/10.1007/s12205-021-1489-4