IDEAS home Printed from https://ideas.repec.org/p/zbw/bubdps/072016.html
   My bibliography  Save this paper

A data-driven selection of an appropriate seasonal adjustment approach

Author

Listed:
  • Webel, Karsten

Abstract

Recent releases of X-13ARIMA-SEATS and JDemetra+ enable their users to choose between the non-parametric X-11 and the parametric ARIMA model-based approach to seasonal adjustment for any given time series without the necessity of switching between different software packages. To ease the selection process, we develop a decision tree whose branches combine conceptual differences between the two methods with empirical issues. The latter primarily include a thorough inspection of the squared gains of final X-11 and Wiener-Kolmogorov seasonal adjustment filters as well as a comparison of various revision measures. We finally illustrate the decision tree on selected German macroeconomic time series.

Suggested Citation

  • Webel, Karsten, 2016. "A data-driven selection of an appropriate seasonal adjustment approach," Discussion Papers 07/2016, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:072016
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/130561/1/857145142.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Osborn, Denise R., 1991. "The implications of periodically varying coefficients for seasonal time-series processes," Journal of Econometrics, Elsevier, vol. 48(3), pages 373-384, June.
    2. Fabio Busetti, 2006. "Tests of seasonal integration and cointegration in multivariate unobserved component models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 419-438.
    3. 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 350-359, October.
    4. Maravall, Agustin, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 155-160, April.
    5. 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.
    6. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    7. D. Pfeffermann, 1994. "A General Method For Estimating The Variances Of X‐11 Seasonally Adjusted Estimators," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(1), pages 85-116, January.
    8. Maravall, Agustin, 1993. "Stochastic linear trends : Models and estimators," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 5-37, March.
    9. Busetti, Fabio & Taylor, A. M. Robert, 2003. "Testing against stochastic trend and seasonality in the presence of unattended breaks and unit roots," Journal of Econometrics, Elsevier, vol. 117(1), pages 21-53, November.
    10. Hylleberg, S. (ed.), 1992. "Modelling Seasonality," OUP Catalogue, Oxford University Press, number 9780198773184.
    11. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España.
    12. Maravall, Agustin, 1987. "Minimum Mean Squared Error Estimation of the Noise in Unobserved Component Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 115-120, January.
    13. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    14. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    15. Busetti, Fabio & Harvey, Andrew, 2003. "Seasonality Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 420-436, July.
    16. 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.
    17. Harvey,Andrew & Koopman,Siem Jan & Shephard,Neil (ed.), 2004. "State Space and Unobserved Component Models," Cambridge Books, Cambridge University Press, number 9780521835954.
    18. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521565882.
    19. Hans Franses, Philip, 1992. "Testing for seasonality," Economics Letters, Elsevier, vol. 38(3), pages 259-262, March.
    20. Maravall, Agustin, 1989. "On the dynamic structure of a seasonal component," Journal of Economic Dynamics and Control, Elsevier, vol. 13(1), pages 81-91, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tomas del Barrio Castro & Denise R. Osborn, 2006. "A Random Walk through Seasonal Adjustment: Noninvertible Moving Averages and Unit Root Tests," Economics Discussion Paper Series 0612, Economics, The University of Manchester.
    2. A Matas-Mir & D R Osborn, 2003. "Seasonal Adjustment and the Detection of Business Cycle Phases," Economics Discussion Paper Series 0304, Economics, The University of Manchester.
    3. 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.
    4. Fok, D. & Franses, Ph.H.B.F. & Paap, R., 2005. "Performance of Seasonal Adjustment Procedures: Simulation and Empirical Results," Econometric Institute Research Papers EI 2005-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    6. Svend Hylleberg, 2006. "Seasonal Adjustment," Economics Working Papers 2006-04, Department of Economics and Business Economics, Aarhus University.
    7. Thornton, Michael A., 2013. "Removing seasonality under a changing regime: Filtering new car sales," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 4-14.
    8. Kaiser Remiro, Regina & Maravall, Agustín, 2000. "Notes on time serie analysis, ARIMA models and signal extraction," DES - Working Papers. Statistics and Econometrics. WS 10058, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Daniel Dzikowski & Carsten Jentsch, 2024. "Structural Periodic Vector Autoregressions," Papers 2401.14545, arXiv.org.
    10. S.M.Husnain Bokhari & Ishaque Ahmed Ansari, 2009. "Seasonal Adjustment Of Some Financial Indicators Of Pakistan," IBT Journal of Business Studies (JBS), Ilma University, Faculty of Management Science, vol. 5(2), pages 107-123.
    11. S. M. Husnain Bokhari & Ishaque Ahmed Ansari, 2009. "Seasonal Adjustment Of Some Financial Indicators Of Pakistan," IBT Journal of Business Studies (JBS), Ilma University, Faculty of Management Science, vol. 5(2), pages 5-3.
    12. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    13. Ghysels, E., 1993. "A Time Series Model with Periodic Stochastic Regime Switching," Cahiers de recherche 9314, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    14. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    15. Silhan, Peter A., 2014. "Income smoothing from a Census X-12 perspective," Advances in accounting, Elsevier, vol. 30(1), pages 106-115.
    16. Tommaso Proietti & Marco Riani, 2009. "Transformations and seasonal adjustment," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 47-69, January.
    17. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
    18. Fabio Busetti, 2006. "Tests of seasonal integration and cointegration in multivariate unobserved component models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 419-438.
    19. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
    20. Huang, Tai-Hsin & Shen, Chung-Hua, 2002. "Seasonal cointegration and cross-equation restrictions on a forward-looking buffer stock model of money demand," Journal of Econometrics, Elsevier, vol. 111(1), pages 11-46, November.

    More about this item

    Keywords

    ARIMA model-based approach; linear filtering; signal extraction; unobserved components; X-11 approach;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bubdps:072016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/dbbgvde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.