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

Qing Zhou, Robert Faff, Karen Alpert

Research output: Contribution to journalArticle

21 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)494-513
Number of pages20
JournalJournal of Corporate Finance
Volume25
Early online date9 Feb 2014
DOIs
Publication statusPublished - 30 Apr 2014

Keywords

  • corporate finance
  • bias-correction
  • dynamic panels
  • estimation
  • panel models
  • corporate finance

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