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An Econometric Approach to General Equilibrium Modeling

In: Handbook of Computable General Equilibrium Modeling

Author

Listed:
  • Jorgenson, Dale W.
  • Jin, Hui
  • Slesnick, Daniel T.
  • Wilcoxen, Peter J.

Abstract

The first objective of this chapter is to present a new approach to econometric modeling of producer behavior. Our key contribution is to represent the rate and biases of technical change by unobservable or latent variables. We 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 our dataset, production is disaggregated into 35 separate commodities produced by one or more of the 35 industries making up the US economy. Our 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 our 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. Our model of producer behavior is the supply side of general equilibrium models of the US. 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.

Suggested Citation

  • Jorgenson, Dale W. & Jin, Hui & Slesnick, Daniel T. & Wilcoxen, Peter J., 2013. "An Econometric Approach to General Equilibrium Modeling," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1133-1212, Elsevier.
    • Handle: RePEc:eee:hacchp:v:1:y:2013:i:c:p:1133-1212
    DOI: 10.1016/B978-0-444-59568-3.00017-1
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    Cited by:

    1. Anderson, Kym & Martin, Will & van der Mensbrugghe, Dominique, 2013. "Estimating Effects of Price-Distorting Policies Using Alternative Distortions Databases," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 877-931, Elsevier.
    2. Aguiar, Angel & Corong, Erwin & van der Mensbrugghe, Dominique, 2020. "The GTAP Recursive Dynamic (GTAP-RD) Model: Version 1.0," Conference papers 333133, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. Yizhou Zhang & Geoffrey J.D. Hewings, 2019. "Fiscal Decentralization – A Cautious Tale," Regional Science Policy & Practice, Wiley Blackwell, vol. 11(1), pages 173-187, March.
    4. Aguiar, Angel & Corong, Erwin L. & van der Mensbrugghe, Dominique & Bekkers, Eddy & Koopman, Robert Bernard & Teh, Robert, 2019. "The WTO Global Trade Model: Technical documentation," WTO Staff Working Papers ERSD-2019-10, World Trade Organization (WTO), Economic Research and Statistics Division.
    5. Jean Chateau & Erwin Corong & Elisa Lanzi & Caitlyn Carrico & Jean Fouré & David Laborde, 2020. "Characterizing Supply-Side Drivers of Structural Change in the Construction of Economic Baseline Projections," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 5(1), pages 109-161, June.
    6. Dong, Qi & Murakami, Tomoaki & Nakashima, Yasuhiro, 2021. "Induced Bias of Technological Change in Agriculture and Structural Transformation: A Translog Cost Function Analysis of Chinese Cereal Production," 2021 Conference, August 17-31, 2021, Virtual 315373, International Association of Agricultural Economists.

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    More about this item

    Keywords

    Rate and bias of technical change; latent variables; Kalman filter; aggregate demand; labor supply; confidence intervals; outcome variables;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O51 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - U.S.; Canada
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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