Propagation of Data Error and Parametric Sensitivity in Computable General Equilibrium Models

Computational Economics, 2011

Posted: 4 May 2011 Last revised: 13 Mar 2016

See all articles by Joshua Elliott

Joshua Elliott

University of Chicago; Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP)

Meredith Franklin

Argonne National Laboratory

Ian Foster

University of Chicago; Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP)

Todd Munson

Argonne National Laboratory; University of Chicago

Margaret S. Loudermilk

U.S. Department of Justice - Antitrust Division

Date Written: January 5, 2011

Abstract

While computable general equilibrium (CGE) models are a well-established tool in economic analyses, it is often difficult to disentangle the effects of policies of interest from that of the assumptions made regarding the underlying calibration data and model parameters. To characterize the behavior of a CGE model of carbon output with respect to two of these assumptions, we perform a large-scale Monte Carlo experiment to examine its sensitivity to base year calibration data and elasticity of substitution parameters in the absence of a policy change. By examining a variety of output variables at different levels of economic and geographic aggregation, we assess how these forms of uncertainty impact the conclusions that can be drawn from the model simulations.We find greater sensitivity to uncertainty in the elasticity of substitution parameters than to uncertainty in the base-year data as the projection period increases. While many model simulations were conducted to generate large output samples, we find that few are required to capture the mean model response of the variables tested. However, characterizing standard errors and empirical probability distribution functions is not possible without a large number of simulations.

Keywords: Computable General Equilibrium Models, Uncertainty, Sensitivity

JEL Classification: C68

Suggested Citation

Elliott, Joshua and Franklin, Meredith and Foster, Ian and Munson, Todd and Loudermilk, Margaret S., Propagation of Data Error and Parametric Sensitivity in Computable General Equilibrium Models (January 5, 2011). Computational Economics, 2011. Available at SSRN: https://ssrn.com/abstract=1830314

Joshua Elliott (Contact Author)

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP) ( email )

5735 S. Ellis Street
Chicago, IL 60637
United States

Meredith Franklin

Argonne National Laboratory ( email )

9700 S. Cass Avenue
Argonne, IL 60439
United States

Ian Foster

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP) ( email )

5735 S. Ellis Street
Chicago, IL 60637
United States

Todd Munson

Argonne National Laboratory ( email )

9700 S. Cass Avenue
Argonne, IL 60439
United States

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Margaret S. Loudermilk

U.S. Department of Justice - Antitrust Division

United States

Register to save articles to
your library

Register

Paper statistics

Abstract Views
69
PlumX Metrics