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Time Series Analysis of Repeated Surveys: The State–space Approach

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  • Moshe Feder

Abstract

Cross sectional estimates from repeated surveys form a time series {yt}. These estimates can be viewed as the sum yt=Yt+et of two processes, {Yt}, the population process and {et}, the survey error process. Serial correlations in the latter series are usually present, mainly due to sample overlap. Other sources of data such as censuses, administrative records and demographic population counts are also available. The state–space modelling approach to the analysis of repeated surveys allows combining information from different sources, incorporating benchmarking constraints in a natural way. Results from these methods seem to compare favourably with those from X‐11‐ARIMA in filtering out survey errors.

Suggested Citation

  • Moshe Feder, 2001. "Time Series Analysis of Repeated Surveys: The State–space Approach," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(2), pages 182-199, July.
    • Handle: RePEc:bla:stanee:v:55:y:2001:i:2:p:182-199
    DOI: 10.1111/1467-9574.00164
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    Cited by:

    1. Du, Rex Yuxing & Kamakura, Wagner A., 2015. "Improving the statistical performance of tracking studies based on repeated cross-sections with primary dynamic factor analysis," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 94-112.
    2. Guy Melard & Gülşah Sedefoğlu, 2020. "Business Surveys And Repeated Surveys: A Simulation-Based Study," Working Papers ECARES 2020-13, ULB -- Universite Libre de Bruxelles.
    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.

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