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A pseudo-panel data model of household electricity demand

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  • Bernard, Jean-Thomas
  • Bolduc, Denis
  • Yameogo, Nadège-Désirée

Abstract

We study the dynamic behaviour of household electricity consumption on the basis of four large independent surveys conducted in the province of Québec from 1989 to 2002. The latter region displays some rather unique features such as the very extensive use of electricity for space heating in a cold climate and the wide range of energy sources used to meet space heating requirements. We adopt Deaton (1985) approach to create 25 cohorts of households that form a pseudo-panel. The cohorts have on average 131 households. The model error terms allow for group heteroskedasticity and serial correlation. Short-run and long-run own and cross-price elasticities are statistically significant. Electricity and natural gas are estimated to be substitutes while electricity and fuel oil are complements, as it may occur in the Quebec context. The estimate of the income elasticity is not significant. Comparisons with related studies are provided.

Suggested Citation

  • Bernard, Jean-Thomas & Bolduc, Denis & Yameogo, Nadège-Désirée, 2011. "A pseudo-panel data model of household electricity demand," Resource and Energy Economics, Elsevier, vol. 33(1), pages 315-325, January.
    • Handle: RePEc:eee:resene:v:33:y:2011:i:1:p:315-325
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    1. Verbeek, Marno & Vella, Francis, 2005. "Estimating dynamic models from repeated cross-sections," Journal of Econometrics, Elsevier, vol. 127(1), pages 83-102, July.
    2. Richard Blundell & Martin Browning & Costas Meghir, 1994. "Consumer Demand and the Life-Cycle Allocation of Household Expenditures," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(1), pages 57-80.
    3. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
    4. Jean-Thomas Bernard & Denis Bolduc & Donald Belanger, 1996. "Quebec Residential Electricity Demand: A Microeconometric Approach," Canadian Journal of Economics, Canadian Economics Association, vol. 29(1), pages 92-113, February.
    5. Verbeek, Marno & Nijman, Theo, 1992. "Can Cohort Data Be Treated as Genuine Panel Data?," Empirical Economics, Springer, vol. 17(1), pages 9-23.
    6. Hsing, Yu, 1994. "Estimation of residential demand for electricity with the cross-sectionally correlated and time-wise autoregressive model," Resource and Energy Economics, Elsevier, vol. 16(3), pages 255-263, August.
    7. Poyer, David A. & Henderson, Lenneal & Teotia, Arvind P. S., 1997. "Residential energy consumption across different population groups: comparative analysis for Latino and non-Latino households in USA," Energy Economics, Elsevier, vol. 19(4), pages 445-463, October.
    8. Dahl, Carol A., 1993. "A survey of energy demand elasticities in support of the development of the NEMS," MPRA Paper 13962, University Library of Munich, Germany.
    9. François Gardes & Christian Loisy, 1998. "La pauvreté selon les ménages : une évaluation subjective et indexée sur leur revenu," Économie et Statistique, Programme National Persée, vol. 308(1), pages 95-112.
    10. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
    11. Kamerschen, David R. & Porter, David V., 2004. "The demand for residential, industrial and total electricity, 1973-1998," Energy Economics, Elsevier, vol. 26(1), pages 87-100, January.
    12. Dergiades, Theologos & Tsoulfidis, Lefteris, 2008. "Estimating residential demand for electricity in the United States, 1965-2006," Energy Economics, Elsevier, vol. 30(5), pages 2722-2730, September.
    13. Arsenault, E. & Bernard, J. -T. & Carr, C. W. & Genest-Laplante, E., 1995. "A total energy demand model of Quebec : Forecasting properties," Energy Economics, Elsevier, vol. 17(2), pages 163-171, April.
    14. Dargay, Joyce M., 2002. "Determinants of car ownership in rural and urban areas: a pseudo-panel analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(5), pages 351-366, September.
    15. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    16. Garcia-Cerrutti, L. Miguel, 2000. "Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms," Resource and Energy Economics, Elsevier, vol. 22(4), pages 355-366, October.
    17. Poyer, David A. & Williams, Martin, 1993. "Residential energy demand: additional empirical evidence by minority household type," Energy Economics, Elsevier, vol. 15(2), pages 93-100, April.
    18. Silk, Julian I. & Joutz, Frederick L., 1997. "Short and long-run elasticities in US residential electricity demand: a co-integration approach," Energy Economics, Elsevier, vol. 19(4), pages 493-513, October.
    19. Browning, Martin & Deaton, Angus & Irish, Margaret, 1985. "A Profitable Approach to Labor Supply and Commodity Demands over the Life-Cycle," Econometrica, Econometric Society, vol. 53(3), pages 503-543, May.
    20. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
    21. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Can cohort data be treated as genuine panel data?," Other publications TiSEM d4eada8f-b91c-4fe7-a58c-7, Tilburg University, School of Economics and Management.
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