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Relationship between Missing Data Likelihoods and Complete Data Restricted Likelihoods for Regression Time Series Models: An Application to Total Ozone Data

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  • Sabyasachi Basu
  • Gregory C. Reinsel

Abstract

The relationship is shown between the likelihood of autoregressive moving average (ARMA) models, or the restricted likelihood of a regression model with ARMA errors with possibly non‐consecutive data, and the restricted likelihood when the missing values are filled in with Os and regression terms are added to account for the missing values. The latter method is also useful to model additive outliers in a time series setting. This relationship is then used to fit the models with standard computer packages and the results are applied to the analysis of total ozone time series data that involve missing values.

Suggested Citation

  • Sabyasachi Basu & Gregory C. Reinsel, 1996. "Relationship between Missing Data Likelihoods and Complete Data Restricted Likelihoods for Regression Time Series Models: An Application to Total Ozone Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(1), pages 63-72, March.
    • Handle: RePEc:bla:jorssc:v:45:y:1996:i:1:p:63-72
    DOI: 10.2307/2986223
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    Cited by:

    1. Neil Shephard & Dacheng Xiu, 2012. "Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices," Economics Papers 2012-W04, Economics Group, Nuffield College, University of Oxford.
    2. Andy Lee & John Yick & Yer Van Hui, 2001. "Sensitivity of the portmanteau statistic in time series modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 691-702.
    3. Baragona, Roberto & Battaglia, Francesco & Calzini, Claudio, 2001. "Genetic algorithms for the identification of additive and innovation outliers in time series," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 1-12, July.

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