Many scholars from multiple professional and academic disciplines have investigated the various links between the construction industry and economic output. Nevertheless, there remains a noticeable dearth of studies that address the potential impact of the players within the construction industry on various economic indicators. The goal of this research is to study how the economic performance of the US-measured in GDP-is impacted by the performance of the construction industry and its key players and how the performance of the construction industry could help in forecasting future US GDP. This goal is achieved by studying the relationship between GDP, total construction spending (TTLCONS), the Standard and Poor's 500 (S&P500) index (GSPC), and the stocks of major publicly traded construction companies. The authors applied an interdependent research methodology that included (1) data collection, (2) statistical testing on the data using correlation analysis and Granger causality testing, and (3) vector autoregression (VAR) for both fitting and prediction purposes. A positive correlation was found between GDP, the S&P500, TTLCONS, and the stocks of major publicly traded construction-related companies. Also, the Granger causality test showed that some major construction company stocks are useful in forecasting GDP. The developed VAR model was used to forecast GDP for 2 years with acceptable accuracy. In this connection, the model was validated by successfully forecasting in a retrospective manner the effect of the 2008 financial crisis. This shows that the stock prices of select publicly traded construction and equipment companies can be used to predict GDP. In fact, a similar model could have been used to predict the 2008 economic collapse and develop ex ante mitigation strategies. The findings of this study could open opportunities for abandoning the notion of studying the construction industry solely using the health of residential construction. As such, this research should help in moving toward the development of a construction-economy nexus.

There is a sizeable amount of research that investigates the relationship between the construction market and the economy. However, the impact of key companies in the construction market on the economic performance still requires more research. The goal of this research is to investigate the connection between the GDP, as a measure of the economic performance, and the stock prices of large publicly traded companies in the construction industry, in the U.S. This is achieved by analyzing the GDP, total construction spending (TTLCONS), S&P 500 Index (GSPC), and the stocks of large companies in the construction field. The analysis methods include: 1) statistical analysis using correlation analysis and Granger causality testing, and 2) vector auto-regression (VAR). Analysis is performed using R. Preliminary results show positive correlations between the GDP, TTLCONS, GSPC, and the stock prices. Granger causality testing showed a satisfactory causality of the stock prices on the GDP. A VAR model was developed and used to predict the GDP for the coming two years. In order to validate the VAR model, it was fitted for historical data before the year 2008 and tested for its ability to predict the 2008 economic crisis. The model correctly predicted the crisis within a satisfactory level of accuracy. This research proposes the significance of investigating the effect of key players in construction on the economy. It presents a new perspective on the relationship between the construction market and economic performance.

The construction industry has long been considered a staple of society in that the industry is imperative for the sustainability of economies. The relationship between the construction industry and macroeconomics has gained popularity in the research and professional literature. The purpose of this paper is to provide an economic performance assessment for the construction industry in the southeastern United States. The authors developed a three-step research methodology. First, descriptive statistics were used to assess the economic output of the southeastern United States, including Alabama, Florida, Georgia, North Carolina, South Carolina, and Tennessee. Second, descriptive statistics and inferential analyses were utilized to assess the construction performance in these six states. Third, forecasts were presented for the construction industry in each state using economic output as the primary construct. When assessing each state's construction sector through economic output [i.e., contribution toward state gross domestic product (GDP), rather than construction volume per se], it becomes apparent that the construction industry is not yet on a path of recovery, especially when viewed from an economic output perspective. For example, where the construction industry used to contribute 6-8% of the output in each state, it now contributes only 3-4%, with these percentages predicted to worsen overall through 2015. Furthermore, the construction industry in each of the six states did have a significant statistical effect on their respective state GDP, and as such, these findings warrant further investigation into leveraging the construction industry as a catalyst for each state's economic output. (C) 2014 American Society of Civil Engineers.

This paper investigates the relationships and interrelationships associated with the commonly accepted U.S. economic indicators and stock prices of major construction equipment manufacturers. The authors developed a three-step research methodology that comprised data collection, hypotheses development, and statistical analysis. Various U.S. economic indicators, i.e. Real Gross Domestic Product, Inflation Rate, Turner Construction Cost Index, price of Gold, and price of Crude Oil, were analyzed relative to the stock prices of U.S. construction equipment manufacturers, i.e. Caterpillar, Deere & Company, and Manitowoc. In general, relationships among the three construction equipment companies did not consistently parallel one another with respect to the various economic indicators. Perhaps one of the most telling findings was that no significant relationships were found between the stock prices of Caterpillar or Manitowoc and GDP or Construction Cost Index. However, there were significant relationships between the stock prices of Deere & Company, GDP, and the Construction Cost index. Equally as telling, Caterpillar stock price did not correlate with the U.S. unemployment rate. Also, it became obvious that the stock price of Caterpillar was clearly different than that of Deere & Company and Manitowoc. In addition, through close inspection of the nearly perfect correlation between GDP and the Construction Cost Index, it appears that the 2007 collapse of the U.S. construction industry, and consequently the U.S. economy, could have been predicted through a casual investigation of construction material and labor costs. This research opens horizons for abandoning the notion of studying the construction industry solely using residential construction, i.e. housing market, realizing that the construction sector involves other significant decision making variables.