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Finite sample inference methods for dynamic energy demand models

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  • 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)

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.

Suggested Citation

  • Jean-Thomas Bernard & Nadhem Idoudi & Lynda Khalaf & Clément Yélou, 2007. "Finite sample inference methods for dynamic energy demand models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(7), pages 1211-1226.
    • Handle: RePEc:jae:japmet:v:22:y:2007:i:7:p:1211-1226
    DOI: 10.1002/jae.996
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    References listed on IDEAS

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    Cited by:

    1. Bernard, Jean-Thomas & Idoudi, Nadhem & Khalaf, Lynda & Yelou, Clement, 2007. "Finite sample multivariate structural change tests with application to energy demand models," Journal of Econometrics, Elsevier, vol. 141(2), pages 1219-1244, December.
    2. Jenny Lye & Joe Hirschberg, 2018. "Ratios of Parameters: Some Econometric Examples," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 51(4), pages 578-602, December.
    3. Juan Urquiza & Christian J. Murray, 2017. "Do Estimated Taylor Rules Suffer from Weak Identification?," Documentos de Trabajo 494, Instituto de Economia. Pontificia Universidad Católica de Chile..
    4. J.G. Hirschberg & J. N. Lye, 2007. "Providing Intuition to the Fieller Method with Two Geometric Representations using STATA and Eviews," Department of Economics - Working Papers Series 992, The University of Melbourne.
    5. Jean-Thomas Bernard & Michael Gavin & Lynda Khalaf & Marcel Voia, 2015. "Environmental Kuznets Curve: Tipping Points, Uncertainty and Weak Identification," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 60(2), pages 285-315, February.
    6. Jean-Thomas Bernard & Ba Chu & Lynda Khalaf & Marcel Voia, 2019. "Non-Standard Confidence Sets for Ratios and Tipping Points with Applications to Dynamic Panel Data," Annals of Economics and Statistics, GENES, issue 134, pages 79-108.
    7. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    8. Jean-Marie Dufour & Emmanuel Flachaire & Lynda Khalaf & Abdallah Zalghout, 2020. "Identification-Robust Inequality Analysis," Cahiers de recherche 03-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    9. Bergamelli, Michele & Bianchi, Annamaria & Khalaf, Lynda & Urga, Giovanni, 2019. "Combining p-values to test for multiple structural breaks in cointegrated regressions," Journal of Econometrics, Elsevier, vol. 211(2), pages 461-482.
    10. Hirschberg, J.G. & Lye, J.N. & Slottje, D.J., 2008. "Inferential methods for elasticity estimates," Journal of Econometrics, Elsevier, vol. 147(2), pages 299-315, December.
    11. J. G. Hirschberg, J. N. Lye & D. J. Slottje, 2008. "Confidence Intervals for Estimates of Elasticities," Department of Economics - Working Papers Series 1053, The University of Melbourne.
    12. Gatta, Valerio & Marcucci, Edoardo & Scaccia, Luisa, 2015. "On finite sample performance of confidence intervals methods for willingness to pay measures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 169-192.
    13. Hirschberg, J.G. & Lye, J.N., 2010. "Two geometric representations of confidence intervals for ratios of linear combinations of regression parameters: An application to the NAIRU," Economics Letters, Elsevier, vol. 108(1), pages 73-76, July.

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