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Nonparametric Regression and Causality Testing:A Monte‐Carlo Study

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  • David Bell
  • Jim Kay
  • Jim Malley

Abstract

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.

Suggested Citation

  • David Bell & Jim Kay & Jim Malley, 1998. "Nonparametric Regression and Causality Testing:A Monte‐Carlo Study," Scottish Journal of Political Economy, Scottish Economic Society, vol. 45(5), pages 528-552, November.
    • Handle: RePEc:bla:scotjp:v:45:y:1998:i:5:p:528-552
    DOI: 10.1111/1467-9485.00111
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