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Non-Response in Dynamic Panel Data Models

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  • Cheti Nicoletti

    (ISER, University of Essex,)

Abstract

This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature. This connection motivates the extension of the missing at random (MAR) and the missing completely at random (MCAR) concepts from a static setting, as introduced by Rubin (1976), to the case of dynamic panel data models. Using this extension of the MAR and MCAR definitions, we emphasize the limits of some tests and procedures, proposed by Little (1988), Diggle (1989), Park and Davis (1993), Taris (1996) and others, to verify the ignorability of the missing data mechanism.

Suggested Citation

  • Cheti Nicoletti, 2002. "Non-Response in Dynamic Panel Data Models," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A5-4, International Conferences on Panel Data.
    • Handle: RePEc:cpd:pd2002:a5-4
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    4. Behncke S, 2009. "How Does Retirement Affect Health?," Health, Econometrics and Data Group (HEDG) Working Papers 09/11, HEDG, c/o Department of Economics, University of York.
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    6. Rene Segers & Philip Hans Franses, 2014. "Panel design effects on response rates and response quality," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 1-24, February.
    7. Kappe, E.R. & Bijwaard, G.E., 2005. "Does work-related training reduce the discrepancy between function requirements and competencies?," Econometric Institute Research Papers EI 2005-42, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Das, J.W.M. & Toepoel, V. & van Soest, A.H.O., 2007. "Can I use a Panel? Panel Conditioning and Attrition Bias in Panel Surveys," Other publications TiSEM 9407cc7a-23f1-49b9-990d-8, Tilburg University, School of Economics and Management.
    9. Lee, Yong-Woo, 2016. "State Dependence, Unobserved Heterogeneity, And Health Dynamics In Korea," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 57(2), pages 195-221, December.
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    12. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    13. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    14. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.
    15. Prieto Suarez, Joaquin, 2021. "Poverty traps and affluence shields: modelling the persistence of income position in Chile," LSE Research Online Documents on Economics 110719, London School of Economics and Political Science, LSE Library.

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    More about this item

    Keywords

    dynamic panel model; attrition; non-response; missing at random; missing completely at random; statistical model reduction;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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