IDEAS home Printed from https://ideas.repec.org/a/csb/stintr/v13y2012i2p243-260.html
   My bibliography  Save this article

Application of rotation methods in sample surveys in Poland

Author

Listed:
  • Jan Kordos

Abstract

The author reviews theory and application of rotation methods in sample surveys in Poland. He begins with reviewing designs of the surveys across time, depending on different objectives, focusing on partial rotation of sub-samples, and considers estimation problems and data quality issues generally. Next, he refers to some articles and books about surveys published over time, starting with Wilks (1940), Patterson (1950), Eckler (1955), Woodruff (1963), Rao and Graham (1964), Bailar (1975), Duncan and Kalton (1987) and Kalton and Citro (1993). He mentions also early Polish papers on rotation methods (Kordos (1966, 1967, 1971, 1982); Lednicki, 1982; Szarkowski and Witkowski, 1994), and concentrates on Polish household surveys, mainly Household Budget Survey (HBS), Labour Force Survey (LFS) and EU Statistics on Living Conditions and Income (EU-SILC). Special attention is devoted to last research on rotation sampling done by Polish sampling statisticians: Ciepiela et al. (2012), Kordos (2002), Kowalczyk (2002, 2003, 2004), Kowalski (2006, 2009), Kowalski and Wesołowski (2012) and Wesołowski (2010). Concluding remarks are given at the end.

Suggested Citation

  • Jan Kordos, 2012. "Application of rotation methods in sample surveys in Poland," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 243-260, June.
  • Handle: RePEc:csb:stintr:v:13:y:2012:i:2:p:243-260
    as

    Download full text from publisher

    File URL: http://index.stat.gov.pl/repec/files/csb/stintr/csb_stintr_v13_2012_i2_n4.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jan Kordos, 2012. "Statistics In Transition" And "Statistics In Transition - New Series " - First Fifteen Years," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(1), pages 197-200, March.
    2. 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.
    3. 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.
    4. Jacek Wesołowski & Przemysław Ciepiela & Małgorzata Wojtyś & Małgorzata Gniado, 2012. "Dynamic K-Composite estimator for an arbitrary rotation scheme," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(1), pages 7-20, March.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Jo Thori Lind, 2005. "Repeated surveys and the Kalman filter," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 418-427, December.
    4. Panayotis Christidis & Elena Navajas Cawood & Martijn Brons & Burkhard Schade & Antonio Soria, 2014. "Future employment in transport: Analysis of labour supply and demand," JRC Research Reports JRC93302, Joint Research Centre.
    5. 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.
    6. 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.
    7. 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.
    8. Jo Thori Lind, 2002. "Small continuous surveys and the Kalman filter," Discussion Papers 333, Statistics Norway, Research Department.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.

    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:csb:stintr:v:13:y:2012:i:2:p:243-260. 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: Beata Witek (email available below). General contact details of provider: https://edirc.repec.org/data/gusgvpl.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.