Economic consequences of aviation system disruptions: A reduced-form computable general equilibrium analysis. (January 2017)

• Record Type: Journal Article
• Authors: Chen, Zhenhua; Rose, Adam Z.; Prager, Fynnwin; Chatterjee, Samrat
• Is Part Of: Transportation research. Volume 95(2017)
• Page Start: 207
• Page End: 226
• Publication Date: 2017-01
• Subjects: Economic consequence analysis -- Aviation system disruptions -- Computable general equilibrium (CGE) modeling -- Reduced-form -- Latin Hypercube sampling; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2016.09.027
• Languages: English
• ISSNs: 0965-8564

Abstract

Highlights: A reduced-form approach to CGE model for economic consequence analysis is develop. Synthetic data from Latin Hypercube sampling with Monte Carlo randomization is used. Behavioral effects and economic resilience are considered. Uncertainties are presented as CDF and RFD. A visual manifestation of the reduced-form approach is programmed in Excel-VBA. Abstract: The state of the art approach to economic consequence analysis (ECA) is computable general equilibrium (CGE) modeling. However, such models contain thousands of equations and cannot readily be incorporated into computerized systems to yield rapid estimates of economic impacts of various types of transportation system failures due to natural hazards, terrorist attacks or technological accidents. This paper presents a reduced-form approach to simplify the analytical content of CGE models and make them more transparent and enhance their utilization potential. The reduced-form CGE analysis is conducted by first running simulations one hundred times, varying key parameters, such as the magnitude of the initial shock, duration, location, behavioral responses, and resilience, according to a Latin Hypercube sampling procedure. Statistical analysis is then applied to the "synthetic data" results in the form of both ordinary least-squares and quantile regression. The analysis yields linear equations that are incorporated into a computerized system and utilized along with Monte Carlo simulation methods for propagating uncertainties in economic consequences. Although our demonstration and discussion focuses on aviation system disruptions caused by terrorist attacks, the approach can be applied to a broad range of threat scenarios.