Literature DB >> 28833319

Informing Ex Ante Event Studies with Macro-Econometric Evidence on the Structural and Policy Impacts of Terrorism.

Jason Nassios1, James A Giesecke1.   

Abstract

Economic consequence analysis is one of many inputs to terrorism contingency planning. Computable general equilibrium (CGE) models are being used more frequently in these analyses, in part because of their capacity to accommodate high levels of event-specific detail. In modeling the potential economic effects of a hypothetical terrorist event, two broad sets of shocks are required: (1) physical impacts on observable variables (e.g., asset damage); (2) behavioral impacts on unobservable variables (e.g., investor uncertainty). Assembling shocks describing the physical impacts of a terrorist incident is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing behavioral impacts is more difficult. Values for behavioral variables (e.g., required rates of return) are typically inferred or estimated by indirect means. Generally, this has been achieved via reference to extraneous literature or ex ante surveys. This article explores a new method. We elucidate the magnitude of CGE-relevant structural shifts implicit in econometric evidence on terrorist incidents, with a view to informing future ex ante event assessments. Ex post econometric studies of terrorism by Blomberg et al. yield macro econometric equations that describe the response of observable economic variables (e.g., GDP growth) to terrorist incidents. We use these equations to determine estimates for relevant (unobservable) structural and policy variables impacted by terrorist incidents, using a CGE model of the United States. This allows us to: (i) compare values for these shifts with input assumptions in earlier ex ante CGE studies; and (ii) discuss how future ex ante studies can be informed by our analysis.
© 2017 Society for Risk Analysis.

Entities:  

Keywords:  Dynamic CGE modeling; economic impact; terrorism

Year:  2017        PMID: 28833319     DOI: 10.1111/risa.12874

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  1 in total

1.  Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber-Physical Attacks on Electricity Distribution Infrastructure Networks.

Authors:  Edward J Oughton; Daniel Ralph; Raghav Pant; Eireann Leverett; Jennifer Copic; Scott Thacker; Rabia Dada; Simon Ruffle; Michelle Tuveson; Jim W Hall
Journal:  Risk Anal       Date:  2019-02-27       Impact factor: 4.000

  1 in total

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