Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends
AbstractNational statistical institutes generally apply design-based techniques like the generalized regression estimator to compile official statistics. These techniques, however, have relatively large design variances in the case of small sample sizes. In such cases, model based small area estimation techniques can be considered to improve the precision of the estimates. A multivariate structural time series model is developed and applied to obtain more precise estimates of the Dutch monthly unemployment rate for six domains. The model takes advantage of sample information from preceding time periods through an appropriate time series model and from other domains by modelling the correlation between the trend components of the time series models for the different domains. The trends of the six domains are cointegrated, which allows the use of a more parsimonious common factor model that is based on three common trends. Although the use of common factor models is a well known approach in econometrics, its application in the context of small area estimation is novel. The standard errors of the direct estimates of the monthly unemployment rates are more than halved with this approach.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 10 ()
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Web page: http://www.elsevier.com/locate/csda
Cointegration; Common factor models; Kalman filter; Small area estimation; State-space models;
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