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Modeling Corner Solutions with Panel Data: Application to Industrial Energy Demand in France

Author

Listed:
  • Alain Bousquet

    (CEA-IDEI, Université des Sciences Sociales Toulouse I)

  • Raja Chakir

    (GREMAQ, Université des Sciences Sociales)

  • Norbert Ladoux

    (CEA-IDEI, Université des Sciences Sociales Toulouse I)

Abstract

This paper is providing an initial empirical application of Lee and Pitt's approach to the problem of corner solutions with panel data. This approach deals with corner solutions in a manner consistent with behavioral theory. Furthermore it allows the use of flexible form cost functions and general error structure. In this model energy demand, at industrial plant level, is the result of a discrete choice of type of energy to consume and a continuous choice to define the demand level. The econometric model is essentially an endogenous switching regime model which require the evaluation of multivariate probability integrals. We estimate the random effect model by maximum likelihood using a panel of industrial French plants. We verify that estimations predict globally well the model and we simulate the effects of prices variations and a CO2 tax on energy demand.

Suggested Citation

  • Alain Bousquet & Raja Chakir & Norbert Ladoux, 2002. "Modeling Corner Solutions with Panel Data: Application to Industrial Energy Demand in France," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C3-2, International Conferences on Panel Data.
    • Handle: RePEc:cpd:pd2002:c3-2
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    Cited by:

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    2. Bousquet, Alain & Ladoux, Norbert, 2006. "Flexible versus designated technologies and interfuel substitution," Energy Economics, Elsevier, vol. 28(4), pages 426-443, July.
    3. Rosario Crinò, 2010. "Service Offshoring and White-Collar Employment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 595-632.
    4. Koutchad, P. & Carpentier, A. & Femenia, F., 2018. "Dealing with corner solutions in multi-crop micro-econometric models: an endogenous regime approach with regime fixed costs," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277530, International Association of Agricultural Economists.
    5. Millimet, Daniel L. & Tchernis, Rusty, 2008. "Estimating high-dimensional demand systems in the presence of many binding non-negativity constraints," Journal of Econometrics, Elsevier, vol. 147(2), pages 384-395, December.
    6. Nitin Mehta, 2015. "A Flexible Yet Globally Regular Multigood Demand System," Marketing Science, INFORMS, vol. 34(6), pages 843-863, November.
    7. Bölük, Gülden & Koç, A. Ali, 2010. "Electricity demand of manufacturing sector in Turkey: A translog cost approach," Energy Economics, Elsevier, vol. 32(3), pages 609-615, May.
    8. Koutchade, Obafèmi Philippe & Carpentier, Alain & Femenia, Fabienne, 2015. "Corner solutions in empirical acreage choice models: an andogeneous switching regime approach with regime fixed cost," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206060, Agricultural and Applied Economics Association.
    9. Vithala R. Rao & Gary J. Russell & Hemant Bhargava & Alan Cooke & Tim Derdenger & Hwang Kim & Nanda Kumar & Irwin Levin & Yu Ma & Nitin Mehta & John Pracejus & R. Venkatesh, 2018. "Emerging Trends in Product Bundling: Investigating Consumer Choice and Firm Behavior," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 107-120, March.
    10. Bello, Mufutau Opeyemi & Solarin, Sakiru Adebola & Yen, Yuen Yee, 2018. "Hydropower and potential for interfuel substitution: The case of electricity sector in Malaysia," Energy, Elsevier, vol. 151(C), pages 966-983.
    11. Rosario Crino, 2006. "Are U.S. White-Collar Really at Risk of Service Offshoring?," KITeS Working Papers 183, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Oct 2006.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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