Advertisement

Energy Demand Model II

  • Nabaz T. KhayyatEmail author
Chapter
  • 537 Downloads
Part of the Green Energy and Technology book series (GREEN)

Abstract

In this chapter the third group of the econometric model is estimated, namely the energy demand model accounting for risk. The model is constructed as in the previous models in two forms: The Cobb-Douglas and the Translog function to allow for consistency and comparability. The Just and Pop production risk function is applied. To estimate the energy demand incorporating risk, different input factors of production are included.

Keywords

Energy Demand Model Translog FGLS Estimator Elastic Mean Yield Component Variables 
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.

References

  1. Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287. doi: 10.2307/1911963.MathSciNetCrossRefzbMATHGoogle Scholar
  2. Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., & Ernst, F. O. (2011). Integrating energy efficiency performance in production management—gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19(6–7), 667–679. doi: 10.1016/j.jclepro.2010.11.011.CrossRefGoogle Scholar
  3. Gallant, A. R. (2008). Nonlinear statistical models. New York: John.Google Scholar
  4. Greene, W. H. (2008). Econometric analysis (7th ed.). Upper Saddle River: Prentice Hall.Google Scholar
  5. Harvey, A. C. (1976). Estimating regression-models with multiplicative heteroscedasticity. Econometrica, 44(3), 461–465. doi: 10.2307/1913974.MathSciNetCrossRefzbMATHGoogle Scholar
  6. Heshmati, A. (2001). Labour demand and efficiency in Swedish savings banks. Applied Financial Economics, 11(4), 423–433. doi: 10.1080/096031001300313983.CrossRefGoogle Scholar
  7. Just, R. E., & Pope, R. D. (1978). Stochastic specification of production functions and economic implications. Journal of Econometrics, 7(1), 67–86. doi: 10.1016/0304-4076(78)90006-4.MathSciNetCrossRefzbMATHGoogle Scholar
  8. Kumbhakar, S. C., & Tveterås, R. (2003). Risk preferences, production risk and firm heterogeneity. Scandinavian Journal of Economics, 105(2), 275–293. doi: 10.1111/1467-9442.t01-1-00009.CrossRefGoogle Scholar
  9. Lovell, C. A. K., & Schmidt, P. (1987). A comparison of alternative approaches to the measurement of productive efficiency. In A. Dogramaci & R. Färe (Eds.), Applications of modern production theory: efficiency and productivity (Vol. 9, pp. 3–32). Netherlands: Springer.Google Scholar
  10. Moss, C. B., Erickson, K. W., Ball, V. E., & Mishra, A. K. (2003). A Translog Cost Function Analysis of U.S. Agriculture: A Dynamic Specification. http://EconPapers.repec.org/RePEc:ags:aaea03:22027.
  11. Saha, A., Havenner, A., & Talpaz, H. (1997). Stochastic production function estimation: Small sample properties of ML versus FGLS. Applied Economics, 29(4), 459–469. doi: 10.1080/000368497326958.CrossRefGoogle Scholar
  12. SAS Institute Inc. (1993). SAS/ETS User’s Guide, Version 6, (2nd ed.). Cary, NC: SAS Institute Inc.Google Scholar
  13. Tveterås, R. (1997). Econometric Modelling of Production Technology Under Risk: The Case of Norwegian Salmon Aquaculture Industry. (PhD Dissertation), Norwegian School of Economics and Business Administration, Bergen, Norway.Google Scholar
  14. Tveterås, R. (1999). Production risk and productivity growth: Some findings for Norwegian Salmon aquaculture. Journal of Productivity Analysis, 12(2 SRC—GoogleScholar), 161–179.Google Scholar
  15. Tveterås, R. (2000). Flexible panel data models for risky production technologies with an application to Salomon aquaculture. Journal of Econometric Reviews, 19(3 SRC—GoogleScholar), 367–389.Google Scholar
  16. White, H. (1980). A Heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817. doi: 10.2307/1912934.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Technology Management, Economics, and Policy Program, College of EngineeringSeoul National UniversitySeoulSouth Korea

Personalised recommendations