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Missing Observations and Additive Outliers in Time Series Models

Author

Listed:
  • Agustín Maravall
  • Daniel Peña

Abstract

The paper deals with estimation of missing observations in possibly nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analysed. Using the inverse or dual autocorrelation function it is seen how estimation of a missing observation is analogous to the removal of an outlier effect; both problems are closely related with the signal plus noise decomposition of the series.

Suggested Citation

  • Agustín Maravall & Daniel Peña, 1996. "Missing Observations and Additive Outliers in Time Series Models," Working Papers 9612, Banco de España.
    • Handle: RePEc:bde:wpaper:9612
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    Cited by:

    1. Syed Abul Basher & Stefano Fachin, 2014. "Investigating long-run demand for broad money in the Gulf Arab countries," Middle East Development Journal, Taylor & Francis Journals, vol. 6(2), pages 199-214, July.
    2. Chin Wen Cheong & Ng Sew Lai & Nurul Afidah Mohmad Yusof & Khor Chia Ying, 2012. "Asymmetric Fractionally Integrated Volatility Modelling of Asian Equity Markets under the Subprime Mortgage Crisis," Journal of Quantitative Economics, The Indian Econometric Society, vol. 10(1), pages 70-84, January.
    3. Alanya-Beltran, Willy, 2022. "Unit roots in lower-bounded series with outliers," Economic Modelling, Elsevier, vol. 115(C).
    4. Mohamed El Hedi Arouri & Jamel Jouini & Nhu Tuyen Le & Duc Khuong Nguyen, 2012. "On the Relationship between World Oil Prices and GCC Stock Markets," Journal of Quantitative Economics, The Indian Econometric Society, vol. 10(1), pages 98-120, January.
    5. Pedro Delicado & Ana Justel, 1997. "Forecasting with missing data: Application to a real case," Economics Working Papers 213, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Gomez, Victor & Maravall, Agustin & Pena, Daniel, 1998. "Missing observations in ARIMA models: Skipping approach versus additive outlier approach," Journal of Econometrics, Elsevier, vol. 88(2), pages 341-363, November.
    7. Delicado, Pedro, 1995. "Predicción con datos faltantes: aplicación a un caso real," DES - Documentos de Trabajo. Estadística y Econometría. DS 3583, Universidad Carlos III de Madrid. Departamento de Estadística.

    More about this item

    Keywords

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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