Outlier Robust Gmm Estimation of Leverage Determinants in Linear Dynamic Panel Data Models

The GMM estimator that is usually employed in the panel data literature, has an unbounded influence function. This means that the estimator is easily influenced by outliers in the data. This paper develops a variant of the GMM estimator that is less sensitive to anomalous observations. Conditions for consistency and asymptotic normality of the robust estimator are presented. The robustness properties of the new estimator are investigated by means of simulation. An empirical illustration is provided, in which the determinants of a firm's capital structure are investigated using a panel of American firms. The application shows that the robust GMM estimator can be a very useful tool in empirical model building