Nonparametric Regression and Causality Testing: A Monte-Carlo Study.

In this paper, the authors propose a new procedure for causality testing using nonparametric additive models. They argue that the major advantage of their proposed method is that it can be used if the underlying data generation process (DGP) is either linear or nonlinear. The authors' results show that the nonparametric testing procedure provides a more robust test of causality. Furthermore, they show that the loss of power associated with the nonparametric procedure is minimal if the true DGP is linear. Copyright 1998 by Scottish Economic Society.