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Identification and EM-Estimation of Panel Data Models with Non-Ignorable Attrition and Refreshment Samples


  • Pierre Hoonhout

    (Vrije Universiteit Amsterdam)

  • Geert Ridder

    (Johns Hopkins University)


The benefits of panel data are well-documented but missing data problems are often more severe. In particular, units that respond in the first wave may drop out of the panel after one or more periods of participation. This paper focuses on identification and Maximum Likelihood estimation of panel data models when the process that governs this so-called attrition is possibly non-ignorable. In that case, conventional estimation procedures are inconsistent. We derive a multi-period nonparametric identification result and propose estimation by an EM-algorithm that exploits the availability of refreshment samples, consisting of new units randomly drawn from the original population. This additional data source reduces the informational incompleteness of the unbalanced panel in case of non-ignorable attrition. The algorithm is stated in terms of a general population model. Issues related to specific standard panel data models are discussed seperately. Problems caused by partially observed time-varying covariates are addressed along the way.

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  • Pierre Hoonhout & Geert Ridder, 2000. "Identification and EM-Estimation of Panel Data Models with Non-Ignorable Attrition and Refreshment Samples," Econometric Society World Congress 2000 Contributed Papers 1569, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1569

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    5. Huh, Keun & Sickles, Robin C, 1994. "Estimation of the Duration Model by Nonparametric Maximum Likelihood, Maximum Penalized Likelihood, and Probability Simulators," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 683-694, November.
    6. Baker, Michael & Melino, Angelo, 2000. "Duration dependence and nonparametric heterogeneity: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 96(2), pages 357-393, June.
    7. James J. Heckman & Christopher R. Taber, 1994. "Econometric Mixture Models and More General Models for Unobservables in Duration Analysis," NBER Technical Working Papers 0157, National Bureau of Economic Research, Inc.
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