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Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures

Author

Listed:
  • Marcellino, M.

Abstract

We suggest a simple non model based procedure to recover a time series from its temporally aggregated realizations. If additional assumptions on the under lying process are intorduced, it is shown that the procedure is related to many of the former proposals in the literature. It can also be easily modified to deal with the estimation of missing observations and outliers, and with forecasting. Some important identification issues are finally discussed.

Suggested Citation

  • Marcellino, M., 1997. "Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures," Economics Working Papers eco97/30, European University Institute.
  • Handle: RePEc:eui:euiwps:eco97/30
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    Cited by:

    1. Massimiliano Marcellino & Oscar Jorda, "undated". "Stochastic Processes Subject to Time-Scale Transformations: An Application to High-Frequency FX Data," Working Papers 164, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Oscar Jordà & Massimiliano Marcellino, 2004. "Time-scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.

    More about this item

    Keywords

    TIME SERIES ; MODELS;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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