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Estimating End-Use Demand : A Bayesian Approach

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Author Info

  • BAUWENS, Luc

    ()
    (CORE, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

  • FIEBIG, Denzil

    (University of Sydney)

  • STEEL, Mark

    (Tilburg University and Universidad Carlos III, Madrid, Spain)

Abstract

Eliminating negative end-use or appliance consumption estimates and incorporating direct metering information into the process of generating these estimates, these are two important aspects of conditional demand analysis (CDA) that will be the focus of this paper. In both cases a Bayesian approach seems a natural way of proceeding. What needs to be invistigated is whether it is aslo a viable and effective approach. In addition, such a framework naturally lends itself to prediction. Our application involves the estimation of electrical appliance consumptions for a sample of Australian households. This application is designed to illustrate the viability of a full Bayesian analysis of the problem.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1992052.

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Date of creation: 01 Jul 1992
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Handle: RePEc:cor:louvco:1992052

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Related research

Keywords: End-use demand; direct metering; non-negative estimates; Bayesian conditional demand analysis;

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Cited by:
  1. Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers 346, Research Department of Statistics Norway.
  2. Muhammad Akmal & David I. Stern, 2001. "The structure of Australian residential energy demand," Working Papers in Ecological Economics 0101, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.
  3. Brencic, Vera & Young, Denise, 2009. "Time-Saving Innovations, Time Allocation, and Energy Use: Evidence from Canadian Households," Working Papers 2009-2, University of Alberta, Department of Economics.
  4. Bartels, Robert & Fiebig, Denzil G., 1995. "Optimal design in end-use metering experiments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 305-309.
  5. Muhammad Akmal & David I. Stern, 2001. "Residential energy demand in Australia: an application of dynamic OLS," Working Papers in Ecological Economics 0104, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.

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