Estimating dynamic panel models in corporate finance

Article history: Received 2 February 2012 Received in revised form 12 September 2012 Accepted 17 September 2012 Available online 24 September 2012 Dynamic panel models play a natural role in several important areas of corporate finance, but the combination of fixed effects and lagged dependent variables introduces serious econometric bias. Several methods of counteracting these biases are available and these methodologies have been tested on small datasetswith independent, normally-distributed explanatory variables. However, no one has evaluated the methods' performance with corporate finance data, in which the dependent variablemay be clustered or censored and independent variablesmay bemissing, correlatedwith one another, or endogenous.We find that the data's properties substantially affect the estimators' performances. We provide evidence about the impact of various data set characteristics on the estimators, so that researchers can determine the best approach for their datasets. © 2012 Elsevier B.V. All rights reserved. JEL classification: G30 C23