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Granger causality in dynamic binary short panel data models

Author

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  • Francesco Bartolucci

    () (Universita' di Perugia)

  • Claudia Pigini

    () (Universita' Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)

Abstract

Strict exogeneity of covariates other than the lagged dependent variable, and conditional on unobserved heterogeneity, is often required for consistent estimation of binary panel data models. This assumption is likely to be violated in practice because of feedback effects from the past of the outcome variable on the present value of covariates and no general solution is yet available. In this paper, we provide the conditions for a logit model formulation that takes into account feedback effects without specifying a joint parametric model for the outcome and predetermined explanatory variables. Our formulation is based on the equivalence between Granger's de nition of noncausality and a modi cation of the Sims' strict exogeneity assumption for nonlinear panel data models, introduced by Chamberlain (1982) and for which we provide a more general theorem. We further propose estimating the model parameters with a recent xed-e ects approach based on pseudo conditional inference, adapted to the present case, thereby taking care of the correlation between individual permanent unobserved heterogeneity and the model's covariates as well. Our results hold for short panels with a large number of cross-section units, a case of great interest in microeconomic applications.

Suggested Citation

  • Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:421
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    References listed on IDEAS

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    More about this item

    Keywords

    binary panel data; fixed effects; feedback effects; pseudo-conditional inference;

    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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