In search of robust methods for dynamic panel data models in empirical corporate finance |
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| Institution: | 1. Department of Banking and Finance, University of Southampton, UK;2. Department of Accounting, Finance, and Management Information Systems, Prairie View A&M University, USA;3. Department of Finance, East Carolina University, USA;4. Department of Finance, University of Massachusetts Lowell, USA;1. School of Management, Xi’an Jiaotong University, 710049, China;2. UQ Business School, The University of Queensland, QLD 4072, Australia |
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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. |
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| Keywords: | Dynamic panel data estimation GMM Bias correction Capital structure Cash holdings |
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