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Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

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  • Jan van den Brakel
  • Xichuan (Mark) Zhang
  • Siu‐Ming Tam

Abstract

A key requirement of repeated surveys conducted by national statistical institutes is the comparability of estimates over time, resulting in uninterrupted time series describing the evolution of finite population parameters. This is often an argument to keep survey processes unchanged as long as possible. It is nevertheless inevitable that a survey process will need to be redesigned from time to time, for example, to improve or update methods or implement more cost‐effective data collection procedures. It is important to quantify the systematic effects or discontinuities of a new survey process on the estimates of a repeated survey to avoid a disturbance in the comparability of estimates over time. This paper reviews different statistical methods that can be used to measure discontinuities and manage the risk due to a survey process redesign.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:1:p:155-175
    DOI: 10.1111/insr.12347
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    References listed on IDEAS

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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    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. Robert E. Yuskavage, 2007. "COnverting Historical Industry Time Series Data from SIC to NAICS," BEA Papers 0085, Bureau of Economic Analysis.
    4. 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.
    5. James Chipperfield & Philip Bell, 2010. "Embedded experiments in repeated and overlapping surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 51-66, January.
    6. Lynn M. R. Ybarra & Sharon L. Lohr, 2008. "Small area estimation when auxiliary information is measured with error," Biometrika, Biometrika Trust, vol. 95(4), pages 919-931.
    7. Jan A. Brakel & Bart Buelens & Harm-Jan Boonstra, 2016. "Small area estimation to quantify discontinuities in repeated sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 229-250, January.
    8. Jan A. Van Den Brakel, 2008. "Design‐based analysis of embedded experiments with applications in the Dutch Labour Force Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 581-613, June.
    9. 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.
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    Cited by:

    1. 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.
    2. Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2020. "A Study of Discontinuity Effects in Regression Inference based on Web-Augmented Mixed Mode Surveys," Research Papers in Economics 2020-03, University of Trier, Department of Economics.

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