IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

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

  • Robert P. Trost
  • G.S. Maddala
  • Hongyi Li

[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. [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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://dx.doi.org/doi:10.3406/ecop.1996.5827
Download Restriction: no

File URL: http://www.persee.fr/articleAsPDF/ecop_0249-4744_1996_num_126_5_5827/ecop_0249-4744_1996_num_126_5_5827.pdf?mode=light
Download Restriction: no

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

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

as
in new window

Handle: RePEc:prs:ecoprv:ecop_0249-4744_1996_num_126_5_5827
Note: DOI:10.3406/ecop.1996.5827
Contact details of provider: Web page: http://www.persee.fr/web/revues/home/prescript/revue/ecop

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Adrian Pagan, 1985. "Two Stage and Related Estimators and Their Applications," Cowles Foundation Discussion Papers 741, Cowles Foundation for Research in Economics, Yale University.
  2. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
  3. Lester D. Taylor, 1975. "The Demand for Electricity: A Survey," Bell Journal of Economics, The RAND Corporation, vol. 6(1), pages 74-110, Spring.
  4. Ziemer, Rod F. & Wetzstein, Michael E., 1983. "A Stein-rule method for pooling data," Economics Letters, Elsevier, vol. 11(1-2), pages 137-143.
  5. Maddala, G S, 1971. "Generalized Least Squares with an Estimated Variance Covariance Matrix," Econometrica, Econometric Society, vol. 39(1), pages 23-33, January.
  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. Donald Robertson & James Symons, 1991. "Some Strange Properties of Panel Data Estimators," CEP Discussion Papers dp0044, Centre for Economic Performance, LSE.
  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 119-29, May.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:prs:ecoprv:ecop_0249-4744_1996_num_126_5_5827. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Equipe PERSEE)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.