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Estimating End-use Demand: a Bayesian Approach

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  • BAUWENS, L.
  • FIEBIG, D. G.
  • STEEL, M. F. J.

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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File URL: http://dx.doi.org/.10.1080/07350015.1994.10510009
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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 RP with number -1090.

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Handle: RePEc:cor:louvrp:-1090

Note: In : Journal of Business and Economic Statistics, 12, (2) 221-231, 1994
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Cited by:
  1. Muhammad Akmal & David I. Stern, 2001. "Residential energy demand in Australia: an application of dynamic OLS," Working Papers in Ecological Economics, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program 0104, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.
  2. Zhang, Fan, 2011. "Distributional impact analysis of the energy price reform in Turkey," Policy Research Working Paper Series 5831, The World Bank.
  3. Bodil M. Larsen & Runa Nesbakken, 2003. "How to quantify household electricity end-use consumption," Discussion Papers, Research Department of Statistics Norway 346, Research Department of Statistics Norway.
  4. 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.
  5. Bartels, Robert & Fiebig, Denzil G., 1995. "Optimal design in end-use metering experiments," Mathematics and Computers in Simulation (MATCOM), Elsevier, Elsevier, vol. 39(3), pages 305-309.
  6. Hanne Marit Dalen & Bodil M. Larsen, 2013. "Residential end-use electricity demand. Development over time," Discussion Papers, Research Department of Statistics Norway 736, Research Department of Statistics Norway.
  7. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 659-678.
  8. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, Elsevier, vol. 26(2), pages 179-200, March.
  9. Muhammad Akmal & David I. Stern, 2001. "The structure of Australian residential energy demand," Working Papers in Ecological Economics, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program 0101, Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program.

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