Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T17:42:24.820Z Has data issue: false hasContentIssue false

Analyse spectrale des chroniques: application à l’étude des procédés d’ajustement saisonnier

Published online by Cambridge University Press:  17 August 2016

Get access

Abstract

Il est généralement admis qu’une chronique peut être décomposée en un certain nombre de composantes qui, additionnées ou multipliées entre elles, nous donnent les valeurs observées. Il est, de plus, admis que ces composantes peuvent être considérées comme indépendantes les unes des autres. Remarquons cependant que si l’orthogonalité entre les composantes peut être démontrée mathématiquement, elle doit être tempérée dans le monde réel. Nous savons, par exemple, qu’une récession peut fort bien être influencée par la période de l’année où elle commence. On ne devrait pas être surpris d’apprendre qu’à côté des composantes distinguées traditionnellement par la théorie – tendance, composante conjoncturelle, mouvement saisonnier et fluctuations accidentelles – une chronique peut en comporter un certain nombre d’autres que la théorie ignore ou ne qualifie pas explicitement. Ce n’est pas le moindre mérite de l’analyse spectrale de montrer, qu’en théorie, le nombre des composantes possibles d’une chronique est quasiment illimité.

Type
Research Article
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1972 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bibliographie

[1.1] Adelman, I., Long Cycles – Fact or Artifact? American Economic Review, (53), June 1965, pp. 444463.Google Scholar
[1.2] Dhrymes, J., Econometrics, Statistical Fondations and Applications. New York, Harper and Row, 1970.Google Scholar
[1.3] Fishman, G.S., Spectral Methods in Econometrics. Cambridge, Mass., Harvard University Press, 1969.Google Scholar
[1.4] Goodman, N.R., On the Joint Estimation of the Spectra, Co-spectrum and Quadrature Spectrum of a Two-Dimensional Gaussian Process, Scientific Paper N° 10, New York, Engineering Statistics Laboratory.Google Scholar
[1.5] Granger, C.W.J., and Hatanaka, , Spectral Analysis of Economic Time Series. Princeton, Princeton University Press, 1964.Google Scholar
[1.6] Granger, C.W.J., The Typical Spectral Shape of an Economic Variable, Econometrica, (34), January 1966, pp. 150161.Google Scholar
[1.7] Hannan, E.J., Time Series Analysis. London, Methuen, 1966.Google Scholar
[1.8] Jenkins, G.M., and Watts, D.G., Spectral Analysis and its Applications. San Francisco, Holden Day, 1968.Google Scholar
[2.1] Nerlove, M., Spectral Analysis of Seasonnal Adjustment Procedures, Econometrica, (32), N° 3, July 1964, pp. 241286.Google Scholar
[2.2] Nerlove, M., A Comparison of a “Hannan” and B.L.S. Seasonnal Adjustment Filters, Journal of American Statistical Association, Vol. 60, N° 310, June 1965.Google Scholar
[2.3] Nerlove, M., and Grether, D.M., Some Properties of “Optimal” Seasonnal Adjustment, Cowles Foundation Discussion Paper, N° 261, December 1968.Google Scholar
[2.4] Rosenblatt, H.M., Spectral Evaluation of B.L.S. and Census Revised Seasonnal Adjustment Procedures, Journal of American Statistical Association, June 1968, pp. 472501.Google Scholar
Programme d’Analyse Spectrale, établi par le Professeur Parzen, E., (University of Stanford, California) entre 1963 et 1966, et successivement modifié en 1969–1970, par C. Deschamps (Société Traction et Electricité, Bruxelles); en 1971–1972, par C. Van Wymeersch (CESAM – ANEC, Louvain).Google Scholar
[1.1] Adelman, I., Long Cycles – Fact or Artifact? American Economic Review, (53), June 1965, pp. 444463.Google Scholar
[1.2] Dhrymes, J., Econometrics, Statistical Fondations and Applications. New York, Harper and Row, 1970.Google Scholar
[1.3] Fishman, G.S., Spectral Methods in Econometrics. Cambridge, Mass., Harvard University Press, 1969.Google Scholar
[1.4] Goodman, N.R., On the Joint Estimation of the Spectra, Co-spectrum and Quadrature Spectrum of a Two-Dimensional Gaussian Process, Scientific Paper N° 10, New York, Engineering Statistics Laboratory.Google Scholar
[1.5] Granger, C.W.J., and Hatanaka, , Spectral Analysis of Economic Time Series. Princeton, Princeton University Press, 1964.Google Scholar
[1.6] Granger, C.W.J., The Typical Spectral Shape of an Economic Variable, Econometrica, (34), January 1966, pp. 150161.Google Scholar
[1.7] Hannan, E.J., Time Series Analysis. London, Methuen, 1966.Google Scholar
[1.8] Jenkins, G.M., and Watts, D.G., Spectral Analysis and its Applications. San Francisco, Holden Day, 1968.Google Scholar
[2.1] Nerlove, M., Spectral Analysis of Seasonnal Adjustment Procedures, Econometrica, (32), N° 3, July 1964, pp. 241286.Google Scholar
[2.2] Nerlove, M., A Comparison of a “Hannan” and B.L.S. Seasonnal Adjustment Filters, Journal of American Statistical Association, Vol. 60, N° 310, June 1965.Google Scholar
[2.3] Nerlove, M., and Grether, D.M., Some Properties of “Optimal” Seasonnal Adjustment, Cowles Foundation Discussion Paper, N° 261, December 1968.Google Scholar
[2.4] Rosenblatt, H.M., Spectral Evaluation of B.L.S. and Census Revised Seasonnal Adjustment Procedures, Journal of American Statistical Association, June 1968, pp. 472501.Google Scholar
Programme d’Analyse Spectrale, établi par le Professeur Parzen, E., (University of Stanford, California) entre 1963 et 1966, et successivement modifié en 1969–1970, par C. Deschamps (Société Traction et Electricité, Bruxelles); en 1971–1972, par C. Van Wymeersch (CESAM – ANEC, Louvain).Google Scholar