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Specification testing with grouped fixed effects

Author

Listed:
  • Pigini, Claudia
  • Pionati, Alessandro
  • Valentini, Francesco
Abstract
We propose a bootstrap generalized Hausman test for the correct specification of unobserved heterogeneity in fixed-effects panel data models. We consider as null hypotheses two scenarios in which the unobserved heterogeneity is either time-invariant or specified as additive individual and time effects. We contrast the standard fixed- effects estimators with the recently developed two-step grouped fixed-effects estimator, that is consistent in the presence of time-varying heterogeneity under minimal specification and distributional assumptions for the unobserved effects. The Hausman test exploits the general formulation for the variance of the vector of contrasts and critical values are computed via parametric percentile bootstrap, so as to account for the non-centrality of the asymptotic χ 2 distribution arising from the incidental parameters and approximation biases. Monte Carlo evidence shows that the test has correct size and good power in both linear and non linear specification.

Suggested Citation

  • Pigini, Claudia & Pionati, Alessandro & Valentini, Francesco, 2023. "Specification testing with grouped fixed effects," MPRA Paper 117821, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:117821
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    References listed on IDEAS

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    More about this item

    Keywords

    Additive effects; Asymptotic bias; Hausman test; Parametric bootstrap; Time-varying heterogeneity;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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