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Calculating the Variance of Seasonally Adjusted Series

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Author Info

  • Burridge, Peter
  • Wallis, Kenneth F

Abstract

This paper considers the use of the Kalam filter to perform the seasonal adjustment and to calculate the variance of the signal extraction error in model-based seasonal adjustment procedures. The steady-state filter covariance is seen to provide a convenient basis for obtaining the variances not only the current adjustment but also subsequent revisions. The method is applied to the unobserved-components model we have recently proposed as a justification of the X-11 method, and to a real economic time series.

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Bibliographic Info

Paper provided by University of Warwick, Department of Economics in its series The Warwick Economics Research Paper Series (TWERPS) with number 251.

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Length: 28 pages
Date of creation: 1984
Date of revision:
Handle: RePEc:wrk:warwec:251

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Cited by:
  1. Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
  2. Koopman, S.J. & Franses, Ph.H.B.F., 2001. "Constructing seasonally adjusted data with time-varying confidence intervals," Econometric Institute Research Papers EI 2001-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Christophe Planas & Alessandro Rossi, 2004. "Can inflation data improve the real-time reliability of output gap estimates?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(1), pages 121-133.
  4. Agustín Maravall & Cristophe Planas, 1996. "Estimation Error and the Specification of Unobserved Component Models," Banco de Espa�a Working Papers 9608, Banco de Espa�a.
  5. William P. Cleveland, 2002. "Estimated variance of seasonally adjusted series," Finance and Economics Discussion Series 2002-15, Board of Governors of the Federal Reserve System (U.S.).

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