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Misspecification Effects in the Analysis of Panel Data

Author

Listed:
  • Vieira Marcel de Toledo

    (Departamento de Estatística e Programa de Pós-Graduação em Economia, Universidade Federal de Juiz de Fora (UFJF), Rua José Lourenço Kelmer, s/n, Campus Universitário, Bairro São Pedro, 36036-900, Juiz de Fora, MG, Brazil.)

  • Smith Peter W.F.

    (Southampton Statistical Sciences Research Institute (S3RI), University of Southampton, Southampton, SO17 1BJ, United Kingdom.)

  • Salgueiro Maria de Fátima

    (Instituto Universitário de Lisboa (ISCTE-IUL), Business Research Unit, Av. Forças Armadas, 1649-026, Lisbon, Portugal.)

Abstract

Misspecification effects (meffs) measure the effect on the sampling variance of an estimator of incorrect specification of both the sampling scheme and the model considered. We assess the effect of various features of complex sampling schemes on the inferences drawn from models for panel data using meffs. Many longitudinal social survey designs employ multistage sampling, leading to some clustering, which tends to lead to meffs greater than unity. An empirical study using data from the British Household Panel Survey is conducted, and a simulation study is performed. Our results suggest that clustering impacts are stronger for longitudinal studies than for cross-sectional studies, and that meffs for the regression coefficients increase with the number of waves analysed. Hence, estimated standard errors in the analysis of panel data can be misleading if any clustering is ignored.

Suggested Citation

  • Vieira Marcel de Toledo & Smith Peter W.F. & Salgueiro Maria de Fátima, 2016. "Misspecification Effects in the Analysis of Panel Data," Journal of Official Statistics, Sciendo, vol. 32(2), pages 487-505, June.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:2:p:487-505:n:13
    DOI: 10.1515/jos-2016-0025
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    References listed on IDEAS

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    1. M. Salgueiro & Peter Smith & Marcel Vieira, 2013. "A multi-process second-order latent growth curve model for subjective well-being," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 735-752, February.
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