IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v29y2008i6p1057-1065.html
   My bibliography  Save this article

Outlier detection in ARMA models

Author

Listed:
  • Hamid Louni

Abstract

. We consider an autoregressive moving‐average (ARMA) time series where the observations are perturbed by two kinds of outliers: an additive outlier (AO) or an innovation outlier (IO). Abraham and Yatawara [Journal of Time Series Analysis (1988) Vol. 9, pp. 109–19] investigate a sequential test which successively detects and identifies the outlier type. In this article, we propose an extension of this test, called ‘modified sequential test’, which performs the two procedures simultaneously and coherently. The asymptotic distribution of the test statistic is calculated under the null hypothesis that no outlier is present. Comparison of the two test procedures using simulation experiments shows that the proposed test gives a better power especially in the case of an IO.

Suggested Citation

  • Hamid Louni, 2008. "Outlier detection in ARMA models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1057-1065, November.
    • Handle: RePEc:bla:jtsera:v:29:y:2008:i:6:p:1057-1065
    DOI: 10.1111/j.1467-9892.2008.00595.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.2008.00595.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.2008.00595.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Francesco Battaglia & Lia Orfei, 2005. "Outlier Detection And Estimation In NonLinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 107-121, January.
    2. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    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. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    2. Olivier Darné & Amélie Charles, 2011. "Large shocks in U.S. macroeconomic time series: 1860-1988," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(1), pages 79-100, January.
    3. Kocenda, Evzen & Valachy, Juraj, 2006. "Exchange rate volatility and regime change: A Visegrad comparison," Journal of Comparative Economics, Elsevier, vol. 34(4), pages 727-753, December.
    4. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.
    5. Min-Hsien Chiang & Ray Yeutien Chou & Li-Min Wang, 2016. "Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 126-144, February.
    6. Francisco JA Cysneiros, 2018. "Symmetric Regression Model for Temporal Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(2), pages 44-45, February.
    7. González-Sánchez, Mariano, 2021. "Is there a relationship between the time scaling property of asset returns and the outliers? Evidence from international financial markets," Finance Research Letters, Elsevier, vol. 38(C).
    8. Alonso Fernández, Andrés Modesto & Peña, Daniel & Romo, Juan, 2000. "Resampling time series by missing values techniques," DES - Working Papers. Statistics and Econometrics. WS 9923, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Victor M. Guerrero & Daniel Peña, 1995. "Linear Combination of Information in Time Series Analysis," Working Papers 9507, Centro de Investigacion Economica, ITAM.
    10. Sánchez, María Jesús & Peña, Daniel, 1997. "The identification of multiple outliers in arima models," DES - Working Papers. Statistics and Econometrics. WS 6220, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Luigi Grossi & Fany Nan, 2018. "The influence of renewables on electricity price forecasting: a robust approach," Working Papers 2018/10, Institut d'Economia de Barcelona (IEB).
    12. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    13. Maravall, Agustín & Peña, Daniel, 1992. "Missing observations and additive outliers in time series models," UC3M Working papers. Economics 2888, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Peña, Daniel, 1995. "Combining information in statistical modelling," DES - Working Papers. Statistics and Econometrics. WS 4516, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    16. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    17. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
    18. Justel, Ana & Peña, Daniel & Sánchez, María Jesús, 1994. "Grupos atípicos en modelos econométricos," DES - Documentos de Trabajo. Estadística y Econometría. DS 10755, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Gómez, Víctor & Maravall, Agustín & Peña, Daniel, 1993. "Computing missing values in time series," DES - Working Papers. Statistics and Econometrics. WS 3737, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Andrés Alonso & Daniel Peña & Juan Romo, 2003. "Resampling time series using missing values techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 765-796, December.

    More about this item

    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:bla:jtsera:v:29:y:2008:i:6:p:1057-1065. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.