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Computing missing values in time series

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  • Gómez, Víctor
  • Maravall, Agustín
  • Peña, Daniel

Abstract

This work presents two algorithms to estimate missing values in time series. The first is the Kalman Filter, as developed by Kohn and Ansley (1986) and others. The second is the additive outlier approach, developed by Pefia, Ljung and Maravall. Both are exact and lead to the same results. However, the first is, in general, faster and the second more flexible.

Suggested Citation

  • 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.
    • Handle: RePEc:cte:wsrepe:3737
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    References listed on IDEAS

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    1. S. R. Brubacher & G. Tunnicliffe Wilson, 1976. "Interpolating Time Series with Application to the Estimation of Holiday Effects on Electricity Demand," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(2), pages 107-116, June.
    2. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    3. Mohsen Pourahmadi, 1989. "Estimation And Interpolation Of Missing Values Of A Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(2), pages 149-169, March.
    4. G. Mélard, 1984. "A Fast Algorithm for the Exact Likelihood of Autoregressive‐Moving Average Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 104-114, March.
    5. Guy Melard, 1984. "Algorithm AS197: A fast algorithm for the exact likelihood of autoregressive-moving average models," ULB Institutional Repository 2013/13692, ULB -- Universite Libre de Bruxelles.
    6. Peña, Daniel & Tiao, George C., 1991. "A Note on likelihood estimation of missing values in time series," UC3M Working papers. Economics 2748, Universidad Carlos III de Madrid. Departamento de Economía.
    7. William Bell & Steven Hillmer, 1991. "Initializing The Kalman Filter For Nonstationary Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 283-300, July.
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    Cited by:

    1. Guerrero, Víctor M. & Peña, Daniel, 1995. "Linear combination of information in time series analysis," DES - Working Papers. Statistics and Econometrics. WS 10340, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. 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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    Keywords

    Kalman filter;

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