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Qualitative Implications of Uncertainty in Economic Equilibrium Models

  • Frederic H. Murphy
  • Suvrajeet Sen
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 128)

Abstract

Many economic equilibrium models have a structure that consists of econometrically estimated demand models and supply models that contain explicit representations of the supply technologies, known as process models. Econometric models measure the consequences of peoples’ decisions and are typically used to estimate demand because it is impossible to represent each individual decision and its consequences. Process modeling is an outgrowth of input-output analysis and linear programming and began with Markowitz [1955]. Here the technologies and possible decisions are modeled explicitly in an optimization model. The solution to the model consists of the decisions of optimizing firms and their consequences. Each modeling approach has had a long history and combining the two types of models into one economic equilibrium model is quite common. Examples are the energy-market models, PIES (Hogan [1975]), IFFS (Murphy, Conti, Sanders and Shaw [1988]), and NEMS (Energy Information Administration [1998]). For a summary of all three, see Murphy and Shaw [1995].

Keywords

Stochastic Program Consumer Surplus Supply Curve Stage Decision Energy Information Administration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Ahn, H. and W.W. Hogan, 1982, “On Convergence of the PIES Algorithm for Computing Equilibria,” Operations Research, 30:281–300.MathSciNetMATHCrossRefGoogle Scholar
  2. Birge, J.R. and F. Louveaux, 1997, Introduction to Stochastic Programming, Springer, New York.MATHGoogle Scholar
  3. Enke, S., 1951, “Equilibrium among Spatially Separated Markets: Solution by Electric Analogue,” Econometrica, 19:40–47.MATHCrossRefGoogle Scholar
  4. Energy Information Administration, 1988, The National Energy Modeling System: An Overview, Washington DC, also available at www.eia.gov.Google Scholar
  5. Georgantzas, N.C. and W. Acar, 1995, Scenario-Driven Planning, Quorum Books, Westport, CT.Google Scholar
  6. Greenberg, H.J. and F.H. Murphy, 1985, “Computing Market Equilibria with Price Regulations Using Mathematical Programming,” Operations Research, 33(5): 935–954.MathSciNetMATHCrossRefGoogle Scholar
  7. Haliassos, M., 1994, “On perfect foresight models in a stochastic world,” The Economic Journal, 104:477–491.CrossRefGoogle Scholar
  8. Hogan W.W., 1975, “Energy Policy Models for Project Independence,” Computers and Operations Research, 2:251–271.CrossRefGoogle Scholar
  9. Kall, P. and S.W. Wallace, 1994, Stochastic Programming, John Wiley and Sons, New York, NY.MATHGoogle Scholar
  10. Markowitz, H.M., 1955 “Concepts and computing procedures for certain X ij programming problems,” in H.A. Antosiewicz (ed.), Proceedings of the Second Symposium in Linear Programming, Vol. 2, NBS and USAF Washington D.C., pp. 509–565.Google Scholar
  11. Murphy, F., J. Conti, R. Sanders, and S. Shaw, 1988, “Modeling and Forecasting Energy Markets with the Intermediate Future Forecasting System,” Operations Research, 36(3):406–420.CrossRefGoogle Scholar
  12. Murphy, F. and S. Shaw, 1995, “The Evolution of Energy Modeling at the Federal Energy Administration and the Energy Information Administration,” Interfaces, 25(5):173–193, September/October.CrossRefGoogle Scholar
  13. Samuelson, P., 1951, “Spatial Price Equilibrium and Linear Programming,” American Economic Review, 42:283–303.Google Scholar
  14. Wallace, S.W., 2000, “Decision Making Under Uncertainty: Is Sensitivity Analysis of any use?” Operations Research, 48(1):20–26.CrossRefGoogle Scholar
  15. Zipkin, P.H., 1980, “Bounds on the Effect of Aggregating Variables in Linear Programs,” Operations Research, 28(2):403–418.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 2002

Authors and Affiliations

  • Frederic H. Murphy
    • 1
  • Suvrajeet Sen
    • 2
  1. 1.MSOM Dept.Temple UniversityPhiladelphiaUSA
  2. 2.SIE Dept.University of ArizonaTucsonUSA

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