IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v179y2016i3p763-791.html
   My bibliography  Save this article

Small area estimation with state space common factor models for rotating panels

Author

Listed:
  • Jan A. Brakel
  • Sabine Krieg

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:3:p:763-791
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssa.12158
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pfeffermann, Danny & Feder, Moshe & Signorelli, David, 1998. "Estimation of Autocorrelations of Survey Errors with Application to Trend Estimation in Small Areas," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 339-348, July.
    2. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, December.
    3. 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.
    4. Andrew Harvey & Chia‐Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
    5. J. Durbin & B. Quenneville, 1997. "Benchmarking by State Space Models," International Statistical Review, International Statistical Institute, vol. 65(1), pages 23-48, April.
    6. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    7. Oksana Bollineni-Balabay & Jan Brakel & Franz Palm, 2016. "Multivariate state space approach to variance reduction in series with level and variance breaks due to survey redesigns," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 377-402, February.
    8. Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 177-177, April.
    9. Pfeffermann, Danny & Tiller, Richard, 2006. "Small-Area Estimation With StateSpace Models Subject to Benchmark Constraints," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1387-1397, December.
    10. Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 163-175, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Caio Gonçalves & Luna Hidalgo & Denise Silva & Jan van den Brakel, 2022. "Single‐month unemployment rate estimates for the Brazilian Labour Force Survey using state‐space models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1707-1732, October.
    2. Jan van den Brakel & Martijn Souren & Sabine Krieg, 2022. "Estimating monthly labour force figures during the COVID‐19 pandemic in the Netherlands," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1560-1583, October.
    3. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
    4. 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.
    5. Danny Pfeffermann, 2022. "Time series modelling of repeated survey data for estimation of finite population parameters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1757-1777, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Danny Pfeffermann, 2022. "Time series modelling of repeated survey data for estimation of finite population parameters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1757-1777, October.
    4. Caio Gonçalves & Luna Hidalgo & Denise Silva & Jan van den Brakel, 2022. "Single‐month unemployment rate estimates for the Brazilian Labour Force Survey using state‐space models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1707-1732, October.
    5. 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.
    6. Caterina Schiavoni & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "A dynamic factor model approach to incorporate Big Data in state space models for official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 324-353, January.
    7. Harm Jan Boonstra & Jan A. Van Den Brakel & Bart Buelens & Sabine Krieg & Marc Smeets, 2008. "Towards small area estimation at Statistics Netherlands," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 21-49.
    8. Jan van den Brakel & Martijn Souren & Sabine Krieg, 2022. "Estimating monthly labour force figures during the COVID‐19 pandemic in the Netherlands," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1560-1583, October.
    9. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    10. Jo Thori Lind, 2005. "Repeated surveys and the Kalman filter," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 418-427, December.
    11. Wanger, Susanne & Weigand, Roland & Zapf, Ines, 2014. "Revision der IAB-Arbeitszeitrechnung 2014 : Grundlagen, methodische Weiterentwicklungen sowie ausgewählte Ergebnisse im Rahmen der Revision der Volkswirtschaftlichen Gesamtrechnungen," IAB-Forschungsbericht 201409, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    12. 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.
    13. Proietti, Tommaso, 2008. "Band spectral estimation for signal extraction," Economic Modelling, Elsevier, vol. 25(1), pages 54-69, January.
    14. Christian Caamaño-Carrillo & Sergio Contreras-Espinoza & Orietta Nicolis, 2023. "Reconstructing the Quarterly Series of the Chilean Gross Domestic Product Using a State Space Approach," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    15. 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.
    16. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
    17. Jo Thori Lind, 2002. "Small continuous surveys and the Kalman filter," Discussion Papers 333, Statistics Norway, Research Department.
    18. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    19. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics 0209002, University Library of Munich, Germany.
    20. Motta, Anderson C. O. & Hotta, Luiz K., 2003. "Exact Maximum Likelihood and Bayesian Estimation of the Stochastic Volatility Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(2), November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:179:y:2016:i:3:p:763-791. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.