Nonparametric regression and causality testing: A Monte-Carlo study

Bell, D., Kay, J. and Malley, J. (1998) Nonparametric regression and causality testing: A Monte-Carlo study. Scottish Journal of Political Economy, 45(5), pp. 528-552. (doi:10.1111/1467-9485.00111)

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In this paper we propose a new procedure for causality testing using nonparametric additive models. We argue that the major advantage of our proposed method is that it can be used if the underlying data generation process (DGP) is either linear or nonlinear. Our results show that the nonparametric testing procedure provides a more robust test of causality. Furthermore, we show that the loss of power associated with the nonparametric procedure is minimal if the true DGP is linear.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Kay, Dr James and Malley, Professor James
Authors: Bell, D., Kay, J., and Malley, J.
College/School:College of Social Sciences > Adam Smith Business School > Economics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Scottish Journal of Political Economy
Publisher:Wiley-Blackwell Publishing Ltd.
ISSN (Online):1467-9485
Published Online:07 January 2003

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