In search of robust methods for dynamic panel data models in empirical corporate finance

Research output: Contribution to journalArticle



Publication details

JournalJournal of Banking and Finance
DateE-pub ahead of print - 30 Dec 2014
DatePublished (current) - Apr 2015
Number of pages15
Pages (from-to)84-98
Early online date30/12/14
Original languageEnglish


We examine which methods are appropriate for estimating dynamic panel data models in empirical corporate finance. Our simulations show that the instrumental variable and GMM estimators are unreliable, and sensitive to the presence of unobserved heterogeneity, residual serial correlation, and changes in control parameters. The bias-corrected fixed-effects estimators, based on an analytical, bootstrap, or indirect inference approach, are found to be the most appropriate and robust methods. These estimators perform reasonably well even in models with fractional dependent variables censored at [0,1]. We verify these results in two empirical applications, on dynamic capital structure and cash holdings.

    Research areas

  • Dynamic panel data estimation; GMM; bias correction; capital structure; cash holdings.

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