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Computing missing values in time series

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URI: http://hdl.handle.net/10016/3737
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ws932721.pdf (360.82 KB)
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1993-10
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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.
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Kalman filter, Additive outliers, Nonstationary ARIMA processes, Concentrated likelihoods
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DES - Working Papers. Statistics and Econometrics. WS