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Wilcoxen chapter on an economic approach to general equilibrium modeling featured in Handbook

Feb 26, 2013

An Econometric Approach to General Equilibrium Modeling

Dale W. Jorgenson, Hui Jin,Daniel T. Slesnick & Peter J. Wilcoxen

Handbook of Computable General Equilibrium Modeling, February 2013

Peter Wilcoxen

Peter Wilcoxen

The first objective of this chapter is to present a new approach to econometric modeling of producer behavior. The authors' key contribution is to represent the rate and biases of technical change by unobservable or latent variables. They also divide the rate of technical change between components that are induced by changes in prices and those that are autonomous and not affected by prices. In the authors' dataset, production is disaggregated into 35 separate commodities produced by one or more of the 35 industries making up the U.S. economy.

The authors' second objective is to present a new econometric model of aggregate consumer behavior. The model allocates full wealth among time periods for households distinguished by demographic characteristics, and determines the within-period demands for leisure, consumer goods and services. An important feature of the authors' approach is the development of a closed-form representation of aggregate demand and labor supply that accounts for the heterogeneity in household behavior that is observed in micro-level data. The authors' model of producer behavior is the supply side of general equilibrium models of the U.S. The aggregate demand functions are important components of the demand side. These general equilibrium models are used to analyze the consequences of a broad spectrum of public policies. These applications are discussed in more detail in Chapter 8 of this Handbook.

The third objective of the chapter is to demonstrate an important benefit of the econometric approach to parameterization. The parameter covariances obtained in the course of estimation can be used to construct confidence intervals for endogenous variables in general equilibrium models. Confidence intervals characterize the precision of modeling results more rigorously and systematically than traditional sensitivity analysis.