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Estimation de la PTF en presence de points aberrants et de points leviers : examen de l'ensemble de donnees KLEMS

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  • Macdonald, Ryan

Abstract

Le present document porte sur l'effet des observations atypiques presentes dans la base de donnees KLEMS (capital, travail, energie, matieres et services) et propose une methode pour le contrer. Le niveau de desagregation, la construction des donnees et les chocs economiques peuvent se traduire par des observations atypiques susceptibles d'influer sur les estimations et sur l'inference si des precautions ne sont pas prises. Les pretests couramment utilises, tels que les tests DFA (Dickey-Fuller ameliore) et KPSS (Kwaitkowski, Phillips, Schmidt et Shin), doivent etre appliques avec prudence dans ce contexte parce qu'ils sont sensibles aux points de donnees inhabituels. En outre, les methodes les plus connues qui sont employees pour etablir des estimations statistiques, comme la methode des moindres carres ordinaires, peuvent ne pas fonctionner correctement en presence d'observations atypiques. Afin de pallier ce probleme, le document illustre une methode robuste d'estimation des relations statistiques.

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Paper provided by Statistics Canada, Direction des etudes analytiques in its series Serie de documents de recherche sur l'analyse economique (AE) with number 2007047f.

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Date of creation: 05 Dec 2007
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Handle: RePEc:stc:stcp5f:2007047f

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Keywords: Methodes statistiques; Comptes economiques; Series chronologiques; Analyse des donnees; Comptes de la productivite;

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  1. Kevin J. Stiroh & Dale W. Jorgenson, 2000. "U.S. Economic Growth at the Industry Level," American Economic Review, American Economic Association, vol. 90(2), pages 161-167, May.
  2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  3. Franses,Philip Hans, 1998. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521586412, November.
  4. Sunil Sapra, 2003. "High-breakdown point estimation of some regression models," Applied Economics Letters, Taylor & Francis Journals, vol. 10(14), pages 875-878.
  5. Kaddour Hadri, 2000. "Testing for stationarity in heterogeneous panel data," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 148-161.
  6. Baldwin, John R. Gu, Wulong, 2007. "La productivite multifactorielle au Canada : une evaluation de diverses methodes d'estimation des services de capital," La revue canadienne de productivite 2007009f, Statistics Canada, Division de l'analyse economique.
  7. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
  8. Galeano, Pedro & Pena, Daniel & Tsay, Ruey S., 2006. "Outlier Detection in Multivariate Time Series by Projection Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 654-669, June.
  9. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
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