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Moment Conditions for AR(1) Panel Data Models with Missing Outcomes

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  • David Pacini
  • Frank Windmeijer

Abstract

We derive moment conditions for dynamic, AR(1) panel data models when values of the outcome variable are missing. In this context, commonly used estimators only use data on individuals observed for at least three consecutive periods. We derive moment conditions for observations with at least three non-consecutive observations for estimation of the parameters by GMM.

Suggested Citation

  • David Pacini & Frank Windmeijer, 2015. "Moment Conditions for AR(1) Panel Data Models with Missing Outcomes," Bristol Economics Discussion Papers 15/660, School of Economics, University of Bristol, UK.
    • Handle: RePEc:bri:uobdis:15/660
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    References listed on IDEAS

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    7. Ahn, Seung C. & Schmidt, Peter, 1997. "Efficient estimation of dynamic panel data models: Alternative assumptions and simplified estimation," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 309-321.
    8. Thomas J. Kniesner & W. Kip Viscusi & Christopher Woock & James P. Ziliak, 2012. "The Value of a Statistical Life: Evidence from Panel Data," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 74-87, February.
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    Cited by:

    1. Sasaki, Yuya & Xin, Yi, 2017. "Unequal spacing in dynamic panel data: Identification and estimation," Journal of Econometrics, Elsevier, vol. 196(2), pages 320-330.

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    More about this item

    Keywords

    Panel Data; Missing Values.;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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