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Combining Panel Data Sets with Attrition and Refreshment Samples

Author

Listed:
  • Keisuke Hirano

    (Harvard University)

  • Guido W. Imbens

    (UCLA)

  • Geert Ridder

    (Vrije Universiteit Amsterdam)

  • Donald B. Rubin

    (Harvard University)

Abstract

This discussion paper resulted in a publication in Econometrica (2001). Volume 69, issue 6, pages 1645-1659. With panel data important issues can be resolved that can not beaddressed with cross--sectional data. A major drawback is that paneldata suffer from more severe missing data problems. Adding a sampleconsisting of new units randomly drawn from the original populationas replacements for units who have dropped out of the panel, aso--called refreshment sample, can be helpful in mitigating theeffects of attrition, both by allowing for estimation of richermodels and by making estimation of conventional models moreprecise. In this paper we develop a family of models thatincorporate refreshment samples, and we demonstrate in an applicationto a Dutch data set on travel behaviour that such models can lead tosubstantially different results than models that assume that themissing data process is ignorable or conventional econometric modelsfor panel data with attrition.

Suggested Citation

  • Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 1998. "Combining Panel Data Sets with Attrition and Refreshment Samples," Tinbergen Institute Discussion Papers 98-033/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:19980033
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    panel data; missing data; attrition.;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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