Is the Information Available from Historical Time Series Data on Economic, Energy, and Construction Market Variables Useful to Explain Variations in ENR Construction Cost Index?
Is the Information Available from Historical Time Series Data on Economic, Energy, and Construction Market Variables Useful to Explain Variations in ENR Construction Cost Index?
复制标题
经济、能源和建筑市场变量的历史时间序列数据中的信息是否有助于解释 ENR 建筑成本指数的变化？
DOI：
10.1061/9780784412329.046
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发表时间：
2012
影响因子：
1.9
通讯作者：
Jian Lu
中科院分区：
文献类型：
--
作者：
B. Ashuri;S. M. Shahandashti;Jian Lu
工程新闻记录 (ENR) 每月发布建筑成本指数 (CCI)。 CCI 被成本工程师广泛用于准备资本项目的成本估算、投标和预算。 CCI 可能会出现重大变化。从一些经济变量（联邦基金利率、消费者价格指数、国内生产总值和失业率）、能源相关变量（原油价格）和建筑市场变量（新屋开工、建筑业就业水平和建筑支出）的历史时间序列数据中获得的信息可能有助于解释 CCI 的变化。本文的研究目的是实证检验上述经济、能源和市场变量的时间序列信息是否有助于解释 CCI 的变化。我们设计了皮尔逊相关性检验和格兰杰因果关系检验等时间序列检验，以统计方式检查每个变量对 CCI 随时间变化的影响。结果表明，消费者价格指数、国内生产总值、原油价格、新屋开工和建筑业就业水平的历史数据信息有助于解释 CCI 的变化。结论是，从联邦基金利率、失业率和建筑支出的历史数据中获得的信息并不能解释 CCI 的变化。这项实证研究预计将加深我们对各种经济、能源和建筑市场变量对 CCI 变化影响的理解。预计这些知识将有助于成本估算者和资本项目规划者解释建筑成本随时间的变化。
Engineering News Record (ENR) publishes Construction Cost Index (CCI) monthly. CCI is widely used by cost engineers for preparing cost estimates, bids, and budgets for capital projects. CCI is subject to significant variations. The information available from historical time series data on some economic variables (federal funds rate, consumer price index, gross domestic product and unemployment rate), energy-related variable (crude oil price), and construction market variables (housing starts, employment level in construction, and construction spending) may be useful for explaining CCI variations. The research objective of this paper is to empirically examine whether the time series information on the above economic, energy, and market variables is useful to explain CCI variations. We devise time series tests like Pearson correlation test and Granger causality test to statistically examine the impact of each variable on CCI variations over time. The results show that the information available from historical data on consumer price index, gross domestic product, crude oil price, housing starts and employment level in construction is useful to explain variations of CCI. It is concluded that the information available from historical data on federal funds rate, unemployment rate, and construction spending do not have power to explain CCI variations. This empirical study is expected to increase our understanding about the impact of various economic, energy, and construction market variables on CCI variations. It is anticipated that this knowledge will be useful for cost estimators and capital project planners to explain changes in construction cost over time.