Bias correction in the estimation of dynamic panel models in corporate finance

Qing Zhou, Robert Faff and Karen Alpert

Journal of Corporate Finance, 2014, vol. 25, issue C, 494-513

Abstract: Dynamic panel models play an increasingly important role in numerous areas of corporate finance research, and a variety of (biased) estimation methods have been proposed in the literature. The biases inherent in these estimation methods have a material impact on inferences about corporate behavior, especially when the empirical model is misspecified. We propose a bias-corrected global minimum variance (GMV) combined estimation procedure to mitigate this estimation problem. We choose the capital structure speed of adjustment (SOA) setting to illustrate the proposed method using both simulated and actual empirical corporate finance data. The GMV estimator non-trivially reduces bias and hence meaningfully increases the reliability of inferences based on parameter estimates. This method can be readily applied to many other corporate finance contexts.

Keywords: Dynamic panels; Corporate finance; Bias-correction (search for similar items in EconPapers)
JEL-codes: C23 G30 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:corfin:v:25:y:2014:i:c:p:494-513

DOI: 10.1016/j.jcorpfin.2014.01.009

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