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Robust analysis of short panels

Author

Listed:
  • Andrew Chesher

    (Institute for Fiscal Studies)

  • Adam Rosen

    (Institute for Fiscal Studies)

  • Yuanqi Zhang

    (University College London)

Abstract

Many structural econometric models include latent variables on whose probability distributions one may wish to place minimal restrictions. Leading examples in panel data models are individual-specific variables sometimes treated as “fixed effects” and, in dynamic models, initial conditions. This paper presents a generally applicable method for characterizing sharp identified sets when models place no restrictions on the probability distribution of certain latent variables and no restrictions on their covariation with other variables. In our analysis latent variables on which restrictions are undesirable are removed, leading to econometric analysis robust to misspecification of restrictions on their distributions which are commonplace in the applied panel data literature. Endogenous explanatory variables are easily accommodated. Examples of application to some static and dynamic binary, ordered and multiple discrete choice and censored panel data models are presented.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Andrew Chesher & Adam Rosen & Yuanqi Zhang, 2024. "Robust analysis of short panels," IFS Working Papers WCWP01/24, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:cwp01/24
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    File URL: https://ifs.org.uk/sites/default/files/2024-01/CWP0124-Robust-analysis-of-short-panels.pdf
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    References listed on IDEAS

    as
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