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Sample Attrition in Panel Data: The Role of Selection on Observables

Author

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  • Robert Moffitt
  • John Fitzgerald
  • Peter Gottschalk

Abstract

The traditional formulation of the attrition problem in econometrics treats it as a special case of the partial-population section bias model in which selection (attrition) is based on model unobservables. This paper considers instead the treatment of attrition as a special case of selection on observables. The analysis compares and contrasts the identification assumptions and estimation procedures for this case with those of the usual case of selection on unobservables. Selection on observables case has rarely been considered in the econometric literature on the problem and hence the framing of the problem in these terms, as presented here, is apparently new. The selection on observables problem is made nontrivial by the assumption that selection occurs on endogenous observables; leading examples are lagged dependent variables from earlier periods in the panel. Among other things, it is shown in the paper that (i) weighted least squares using estimated attrition equations to construct the weights is one method of consistent estimation in thise case; (ii) simply conditioning on the observables does not, by itself, generate consistent estimates; and (iii) that the model is closely related to the choice-based sampling model.

Suggested Citation

  • Robert Moffitt & John Fitzgerald & Peter Gottschalk, 1999. "Sample Attrition in Panel Data: The Role of Selection on Observables," Annals of Economics and Statistics, GENES, issue 55-56, pages 129-152.
  • Handle: RePEc:adr:anecst:y:1999:i:55-56:p:129-152
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    File URL: http://www.jstor.org/stable/20076194
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    Cited by:

    1. Arrighi, Y. & Rapp, T. & Sirven, N., 2017. "The impact of economic conditions on the disablement process: A Markov transition approach using SHARE data," Health Policy, Elsevier, vol. 121(7), pages 778-785.
    2. Halliday, Timothy, 2006. "Migration, Risk, and Liquidity Constraints in El Salvador," Economic Development and Cultural Change, University of Chicago Press, vol. 54(4), pages 893-925, July.
    3. De Vreyer, Philippe & Nilsson, Björn, 2019. "When solidarity fails: Heterogeneous effects on children from adult deaths in Senegalese households," World Development, Elsevier, vol. 114(C), pages 73-94.
    4. repec:spr:portec:v:1:y:2002:i:2:d:10.1007_s10258-002-0008-x is not listed on IDEAS
    5. Andrew Foster & Sveta Milusheva, 2015. "Household Recombination, Retrospective Evaluation, and the Effects of a Health and Family Planning Intervention," Working Papers id:7183, eSocialSciences.
    6. Junning Cai & PingSun Leung & James Mak, 2005. "Tourism's Forward and Backward Linkages," Working Papers 200516, University of Hawaii at Manoa, Department of Economics.
    7. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition, and stratification," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 117-139, August.
    8. Badi Baltagi & Seuck Song, 2006. "Unbalanced panel data: A survey," Statistical Papers, Springer, vol. 47(4), pages 493-523, October.
    9. Morales, Leonardo Fabio & Gordon-Larsen, Penny & Guilkey, David, 2016. "Obesity and health-related decisions: An empirical model of the determinants of weight status across the transition from adolescence to young adulthood," Economics & Human Biology, Elsevier, vol. 23(C), pages 46-62.
    10. repec:eee:jhecon:v:54:y:2017:i:c:p:66-78 is not listed on IDEAS
    11. Sasaki, Yuya, 2015. "Heterogeneity and selection in dynamic panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 236-249.

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