Dynamic Capital Structure Adjustment: Which estimator yields consistent and efficient estimates?

Abstract

The partial adjustment model is key to a number of corporate finance research areas. The model is by its nature an autoregressive-distributed lag model that is characterised by heterogeneity among individuals and autocorrelation due to the presence of the lagged dependent variable. Finding a suitable estimator to fit the model can be challenging, as the existing estimators differ significantly in their consistency and bias. This study used data drawn from 143 non-financial firms listed on the Johannesburg Stock Exchange (JSE) to test for the consistency and efficiency of the leading partial adjustment model estimators. The study results confirm the bias-corrected least squares dummy variable (LSDVC) initialised by the system generalised method of moments (GMM) estimator, the random effects Tobit estimator and the system GMM estimator as the most suitable estimators for the partial adjustment model. The difference GMM estimator and the Anderson-Hsiao instrumental variables estimator are inconsistent and biased in the context of the partial adjustment model.