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Estimation of Autocorrelations of Survey Errors with Application to Trend Estimation in Small Areas

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  • Pfeffermann, Danny
  • Feder, Moshe
  • Signorelli, David

Abstract

This article describes a simple method of estimating the autocorrelations of survey errors for the case of routing-panel sampling designs. Such designs are in routine use, for example, in labor force surveys (LFS). The survey-error autocorrelations induce spurious trends that need to be separated from the trend of the population values when the latter is of interest. This can be achieved by use of simple state-space models that combine the model holding for the population values with the model implied for the survey errors by the estimated autocorrelations. The procedure is illustrated using data from the Australian LFS.

Suggested Citation

  • 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.
    • Handle: RePEc:bes:jnlbes:v:16:y:1998:i:3:p:339-48
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    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. 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.
    3. 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.
    4. 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.
    5. Balakrishna, N. & Kim, Jiwoong & Koul, Hira L., 2020. "Lack-of-fit of a parametric measurement error AR(1) model," Statistics & Probability Letters, Elsevier, vol. 166(C).
    6. Danny Pfeffermann & Richard Tiller, 2005. "Bootstrap Approximation to Prediction MSE for State–Space Models with Estimated Parameters," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 893-916, November.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

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