In Search of Robust Methods for Dynamic Panel Data Models in Empirical Corporate Finance

V.A. Dang, M. Kim, Y. Shin

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Abstract

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.
Original languageEnglish
Pages (from-to)84-98
Number of pages14
JournalJournal of Banking & Finance
Volume53
DOIs
Publication statusPublished - 2015

Keywords

  • Empirical Corporate Finance; Dynamic Panel Data Estimation; Instrumental Variables; GMM; Bias Correction; Capital Structure; Cash Holdings.

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