A Note on likelihood estimation of missing values in time series

George C. Tiao
Authors registered in the RePEc Author Service: Daniel Peña

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

Abstract: Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood, or as random variables and predicted by the expectation of the unknown values given the data. The difference between these two procedures is illustrated by an example. It is argued that the second procedure is, in general, more relevant for estimating missing values in time series.

Keywords: ARIMA; models; Interpolation; Mean; Square; Error (search for similar items in EconPapers)
Date: 1991-02
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:2748

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