Finite sample inference methods for dynamic energy demand models

This file is part of IDEAS, which uses RePEc data

Finite sample inference methods for dynamic energy demand models

Author Info

Jean-Thomas Bernard (Holder of the Chair on the Economics of Electric Energy; Groupe de recherche en économie de l'énergie, de l'environnement et des ressources naturelles [GREEN], Université Laval. Mailing address: Pavillon J.-A.-De Sève, Ste-Foy, Québec, Canada, G1K 7P4)
Nadhem Idoudi (Conseiller Coûts et caractéristiques de la consommation, Direction des Affaires réglementaires et tarifaires, Hydro-Québec Distribution, and Groupe de recherche en économie de l'énergie, de l'environnement et des ressources naturelles [GREEN], Université Laval. Mailing address: 75, boul. René-Lévesque ouest, 2e étage, Montréal, (Québec) H2Z 1A4)
Lynda Khalaf (Holder of the Canada Research Chair in Environment; Centre interuniversitaire de recherche en économie quantitative (CIREQ) and Economics Department, Carleton University, Loeb Building 1125 Colonel By Drive, Ottawa, Ont., Canada K1S 5B6)
Clément Yélou (Département d'économique, Groupe de recherché en économie de l'énergie, de l'environement et des ressources naturelles [GREEN], and Centre de recherche en économie agroalimentaire (CRÉA), Université Laval. Jean-Talon Building, B-5, Statistics Canada, 170 Tunney's Pasture Driveway, Ottawa ON K1A 0T6)

Additional information is available for the following registered author(s):

Clément Yélou

Abstract

This paper considers finite sample motivated inference methods in dynamic energy demand models, in which case commonly used econometric methods remain asymptotic. We focus on structural stability, and on exact confidence set estimation of elasticities. We account for intractable and nuisance parameter dependant distributions through Monte Carlo test procedures. For long-run elasticities which depend on parameter ratios, we assess available asymptotic and exact methods with Fieller based alternatives. Fieller based and exact methods invert approximate and exact relevant test criteria (respectively) and may lead to unbounded set estimates. Our empirical results underscore the importance of using identification-robust inference methods. Copyright © 2007 John Wiley & Sons, Ltd.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 22 (2007)
Issue (Month): 7 ()
Pages: 1211-1226
Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Handle: RePEc:jae:japmet:v:22:y:2007:i:7:p:1211-1226

Contact details of provider:
Web page: http://www.interscience.wiley.com/jpages/0883-7252/

Order Information:
Email:
Web: http://www3.interscience.wiley.com/jcatalog/subscribe.jsp?issn=0883-7252

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

Statistics
Access and download statistics

Did you know? It is the publishers that input data about their publications, as there is no staff at RePEc.

This page was last updated on 2008-9-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.