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Is Seasonal Adjustment a Linear or Nonlinear Data Filtering Process?

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Author Info
Eric Ghysels ()
Clive W.J. Granger
Pierre L. Siklos

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Abstract

In this paper, we investigate whether seasonal adjustment procedures are, at least approximately, linear data transformations. This question is important with respect to many issues including estimation of regression models with seasonally adjusted data. We focus on the X-11 program and first review the features of the program that might be potential sources of nonlinearity. We rely on simulation evidence, involving linear unobserved component ARIMA models, to assess the adequacy of the linear approximation. We define a set of properties for the adequacy of a linear approximation to a seasonal adjustment filter. These properties are examined through statistical tests. Next, we study the effect of X-11 seasonal adjustment on regression statistics assessing the statistical significance of the relationship between economic variables in the same spirit as Sims (1974) and Wallis (1974). These findings are complemented with several empirical examples involving economic data.

Nous examinons si la procédure d'ajustement X-11 est approximativement linéaire. Il y a potentiellement plusieurs sources de non-linéarité dans cette procédure. Le but de l'étude est de savoir si ces sources sont effectivement assez importantes pour affecter, par exemple, des résultats d'estimation dans des modèles de régression linéaire. La seule façon de répondre à cette question est par estimation. Nous proposons plusieurs critères qu'on peut utiliser pour juger si une procédure d'ajustement est approximativement linéaire. Nous examinons également par simulation des propriétés de tests dans le modèle de régression dans le même esprit que Sims (1974) et Wallis (1974).

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Paper provided by CIRANO in its series CIRANO Working Papers with number 95s-19.

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Date of creation: 01 Mar 1995
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Handle: RePEc:cir:cirwor:95s-19

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Related research
Keywords: X-11 program; Nonlinearity; X-11 program ; Nonlinearity;

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Vogelsang, Timothy J & Perron, Pierre, 1998. "Additional Tests for a Unit Root Allowing for a Break in the Trend Function at an Unknown Time," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1073-1100, November.
    Other versions:
  2. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March. [Downloadable!] (restricted)
  3. repec:att:wimass:199520 is not listed on IDEAS
  4. Ghysels, E., 1993. "Seasonal Adjustment and Other Data Transformations," Cahiers de recherche 9322, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    Other versions:
  5. Neil R. Ericsson & David F. Hendry & Hong-Anh Tran, 1993. "Cointegration, seasonality, encompassing, and the demand for money in the United Kingdom," International Finance Discussion Papers 457, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  6. Burridge, Peter & Wallis, Kenneth F, 1984. "Unobserved-Components Models for Seasonal Adjustment Filters," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 350-59, October.
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  7. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November. [Downloadable!] (restricted)
  8. Maravall, Agustin, 1988. "A note on minimum mean squared error estimation of signals with unit roots," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 589-593. [Downloadable!] (restricted)
  9. Peter Burridge & Kenneth Wallis, 1988. "Prediction theory for autoregressivemoving average processes," Econometric Reviews, Taylor and Francis Journals, vol. 7(1), pages 65-95. [Downloadable!] (restricted)
  10. Ghysels, Eric & Perron, Pierre, 1993. "The effect of seasonal adjustment filters on tests for a unit root," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 57-98. [Downloadable!] (restricted)
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  11. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
  12. Grether, D M & Nerlove, M, 1970. "Some Properties of 'Optimal' Seasonal Adjustment," Econometrica, Econometric Society, vol. 38(5), pages 682-703, September. [Downloadable!] (restricted)
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  13. Marcel Boyer & Jean-Jacques Laffont, 1994. "Environmental Risks and Bank Liability," CIRANO Working Papers 94s-22, CIRANO. [Downloadable!]
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  14. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April. [Downloadable!] (restricted)
  15. Ghysels, E. & Lieberman, O., 1993. "Dynamic Regression and Filtered Data Series: A Laplace Approximation to the Effects of Filtering in Small Samples," Cahiers de recherche 9335, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Lawrence J. Christiano & Richard M. Todd, 2000. "The Conventional Treatment of Seasonality in Business Cycle Analysis: Does it Create Distortions?," NBER Technical Working Papers 0266, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  2. Antonio Matas-Mir & Denise R. Osborn & Marco J. Lombardi, 2008. "The effect of seasonal adjustment on the properties of business cycle regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 257-278. [Downloadable!]
    Other versions:
  3. Cubadda, Gianluca & Omtzigt, Pieter, 2003. "Small Sample Improvements in the Statistical Analysis of Seasonally Cointegrated Systems," Economics & Statistics Discussion Papers esdp03012, University of Molise, Dept. SEGeS. [Downloadable!]
    Other versions:
  4. R. Tschernig, . "Nonlinearities in German Unemployment Rates: A Nonparametric Analysis," Sonderforschungsbereich 373 1996-45, Humboldt Universitaet Berlin.
  5. Philip Kostov & John Lingard, 2005. "Seasonally specific model analysis of UK cereals prices," Econometrics 0507014, EconWPA. [Downloadable!]
  6. Eric Ghysels & Clive W.J. Granger & Pierre L. Siklos, 1997. "Seasonal Adjustment and Volatility Dynamics," CIRANO Working Papers 97s-39, CIRANO. [Downloadable!]
  7. Gianluca Cubadda, 2001. "Common Features In Time Series With Both Deterministic And Stochastic Seasonality," Econometric Reviews, Taylor and Francis Journals, vol. 20(2), pages 201-216. [Downloadable!] (restricted)
  8. Antonio Matas Mir & Denise R Osborn, 2004. "Seasonal adjustment and the detection of business cycle phases," Working Paper Series 357, European Central Bank. [Downloadable!]
    Other versions:
  9. Zacharias Psaradakis & Martin Sola, 2003. "On detrending and cyclical asymmetry," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(3), pages 271-289. [Downloadable!]
    Other versions:
  10. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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