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Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends

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  • Krieg, Sabine
  • van den Brakel, Jan A.

Abstract

National 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.

Suggested Citation

  • Krieg, Sabine & van den Brakel, Jan A., 2012. "Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2918-2933.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:10:p:2918-2933
    DOI: 10.1016/j.csda.2012.02.008
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    Cited by:

    1. Jan A. Brakel & Sabine Krieg, 2016. "Small area estimation with state space common factor models for rotating panels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 763-791, June.
    2. Susanne Wanger & Roland Weigand & Ines Zapf, 2016. "Measuring hours worked in Germany – Contents, data and methodological essentials of the IAB working time measurement concept [Die Berechnung der geleisteten Arbeitsstunden in Deutschland – Inhalte,," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 49(3), pages 213-238, November.
    3. Wanger, Susanne & Weigand, Roland & Zapf, Ines, 2016. "Measuring hours worked in Germany : contents, data and methodological essentials of the IAB working time measurement concept (Die Berechnung der geleisteten Arbeitsstunden in Deutschland : Inhalte, Da," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 49(3), pages 213-238.
    4. Jan van den Brakel & Xichuan (Mark) Zhang & Siu‐Ming Tam, 2020. "Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 155-175, April.
    5. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    6. Wanger, Susanne & Weigand, Roland & Zapf, Ines, 2016. "Measuring hours worked in Germany : contents, data and methodological essentials of the IAB working time measurement concept (Die Berechnung der geleisteten Arbeitsstunden in Deutschland : Inhalte, Da," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 49(3), pages 213-238.
    7. Oksana Bollineni‐Balabay & Jan van den Brakel & Franz Palm & Harm Jan Boonstra, 2017. "Multilevel hierarchical Bayesian versus state space approach in time series small area estimation: the Dutch Travel Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1281-1308, October.

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