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Missing Observations and Additive Outliers in Time Series Models


  • Agustín Maravall
  • Daniel Peña


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;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:9612

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    References listed on IDEAS

    1. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    2. Nerlove, Marc & Grether, David M. & Carvalho, José L., 1979. "Analysis of Economic Time Series," Elsevier Monographs, Elsevier, edition 1, number 9780125157506 edited by Shell, Karl.
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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. 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.
    3. 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.
    4. 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.

    More about this item



    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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