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Random Error and Simulation Models With an Unobserved Dependent Variable as applied to the Benefits and Costs of the Clean Air Act

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Author Info
Scott Farrow () (UMBC and The Woods Hole Oceanographic Institution)

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Abstract

Most empirical simulation models used in benefit-cost or risk analysis investigate uncertainty based on variability in parameters and conditioning factors. A pure random error term is frequently omitted. Ex-ante benefit-cost analyses create a particular problem because there are not historically observed values of the dependent variable, such as net present value. An estimator for the error variance is developed based on analysis of variance measures and R-squared. When applied to a model of the net benefits of the Clean Air Act, the probability that the net present value is negative increases from .2 to 4.5 percent.

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File URL: http://www.umbc.edu/economics/wpapers/wp_09_102_CWISFarrow.pdf
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Publisher Info
Paper provided by UMBC Department of Economics in its series UMBC Economics Department Working Papers with number 09-103.

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Length: 9 pages
Date of creation:
Date of revision: 26 Jan 2008
Handle: RePEc:umb:econwp:09103

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Postal: UMBC Department of Economics 1000 Hilltop Circle Baltimore MD 21250, USA
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Related research
Keywords: Simulation; random error; benefit-cost;

Find related papers by JEL classification:
H4 - Public Economics - - Publicly Provided Goods
C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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  1. Spash, Clive L., 2007. "Deliberative monetary valuation (DMV): Issues in combining economic and political processes to value environmental change," Ecological Economics, Elsevier, vol. 63(4), pages 690-699, September. [Downloadable!] (restricted)
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This page was last updated on 2009-12-7.


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