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Aggregation and Disaggregation of Structural Time Series Models

Author

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  • Luiz K. Hotta
  • Klaus L. Vasconcellos

Abstract

The aggregation/disaggregation problem has been widely studied in the time series literature. Some main issues related to this problem are modelling, prediction and robustness to outliers. In this paper we look at the modelling problem with particular interest in the local level and local trend structural time series models together with their corresponding ARIMA(0, 1, 1) and ARIMA(0, 2, 2) representations. Given an observed time series that can be expressed by a structural or autoregressive integrated moving‐average (ARIMA) model, we derive the necessary and sufficient conditions under which the aggregate and/or disaggregate series can be expressed by the same class of model. Harvey's cycle and seasonal components models (Harvey, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press, 1989) are also briefly discussed. Systematic sampling of structural and ARIMA models is also discussed.

Suggested Citation

  • Luiz K. Hotta & Klaus L. Vasconcellos, 1999. "Aggregation and Disaggregation of Structural Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(2), pages 155-171, March.
    • Handle: RePEc:bla:jtsera:v:20:y:1999:i:2:p:155-171
    DOI: 10.1111/1467-9892.00131
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    Cited by:

    1. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 93-107, March.
    2. Bu Hyoung Lee, 2022. "Bootstrap Prediction Intervals of Temporal Disaggregation," Stats, MDPI, vol. 5(1), pages 1-13, February.
    3. Baoline Chen, 2007. "An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts," BEA Papers 0077, Bureau of Economic Analysis.

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