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Estimation des élasticités de court et de long termes de la demande d'électricité sur données de panel à partir d'estimateurs à rétrécisseur

Listed author(s):
  • Hongyi Li
  • G.S. Maddala
  • Robert P. Trost

[fre] Estimation des élasticités de court et de long termes de la demande d'électricité sur données de panel à partir d'estimateurs à rétrécisseurs par Hongyi Li, G.S. Maddala et Robert P. Trost On peut distinguer trois approches dans l'analyse des données de panel. L'estimation séparée des paramètres sur chaque série individuelle,1 l'estimation sur données empilées, avec ou sans effets spécifiques, et l'estimation sur données empilées en admettant un certain degré d'hétérogénéité, comme dans le modèle à coefficients aléatoires. Dans ce dernier cas, c'est la moyenne des coefficients des' séries individuelles qui nous intéresse. Cet article présente dans un cadre unifié,' les estimateurs relevant de cette troisième catégorie» en recourant à la fois à l'approche classique, à l'approche bayésienne empirique et à l'approche bayésienne classique. Ces méthodes sont illustrées par l'estimation d'élasticités de la demande d'électricité et de gaz naturel des ménages, aux États-Unis. [eng] Estimation of Short Run and Long Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators by Hongyi Li, G.S. Maddala and Robert P. Trost In the analysis of panel data, there are, broadly speaking, three approaches: to present separate estimates of the parameters in each cross -section, present pooled estimates with or without cross-section specific effects, or present a pooled estimator assuming some, but not complete, heterogeneity, as in the random coefficient model. In the last case, we would be interested in the mean of the coefficients for the cross-sections, or the separate coefficients themselves. The paper presents in a unified framework the estimators in this last case, using both the classical, empirical Bayes, and Bayes methods. The methods are illustrated with estimation of elasticities of residential demand for electricity and natural gas in the U.S.

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Article provided by Programme National Persée in its journal Économie & prévision.

Volume (Year): 126 (1996)
Issue (Month): 5 ()
Pages: 127-141

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Handle: RePEc:prs:ecoprv:ecop_0249-4744_1996_num_126_5_5827
Note: DOI:10.3406/ecop.1996.5827
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  1. Adrian Pagan, 1986. "Two Stage and Related Estimators and Their Applications," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 517-538.
  2. Ziemer, Rod F. & Wetzstein, Michael E., 1983. "A Stein-rule method for pooling data," Economics Letters, Elsevier, vol. 11(1-2), pages 137-143.
  3. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  4. Maddala, G S, 1971. "Generalized Least Squares with an Estimated Variance Covariance Matrix," Econometrica, Econometric Society, vol. 39(1), pages 23-33, January.
  5. Robertson, D & Symons, J, 1992. "Some Strange Properties of Panel Data Estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(2), pages 175-189, April-Jun.
  6. Alogoskoufis, George & Smith, Ron, 1991. " On Error Correction Models: Specification, Interpretation, Estimation," Journal of Economic Surveys, Wiley Blackwell, vol. 5(1), pages 97-128.
  7. Lester D. Taylor, 1975. "The Demand for Electricity: A Survey," Bell Journal of Economics, The RAND Corporation, vol. 6(1), pages 74-110, Spring.
  8. Nickell, Stephen, 1985. "Error Correction, Partial Adjustment and All That: An Expository Note," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 47(2), pages 119-129, May.
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