Abstract
This paper considers a dynamic extension of the classical error components model based on the ideas of structural time series models. The study concentrates on the mean square error estimation of time-dependent means by using the Kalman filter, and on the relative efficiency of these estimators as a function of both the number of observations across units and time.
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I would like to thank Professor Andrew C. Harvey for helpful comments. The research was partially supported by the Dirección de Investigación Pontificia Universidad Católica de Chile as part of the project DIUC 90037E.
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Marshall, P. Estimating time-dependent means in dynamic models for cross-sections of time series. Empirical Economics 17, 25–33 (1992). https://doi.org/10.1007/BF01192472
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DOI: https://doi.org/10.1007/BF01192472