Testing the significance of categorical predictor variables in nonparametric regression models Academic Article uri icon


  • In this paper we propose a test for the significance of categorical predictors in nonparametric regression models. The test is fully data-driven and employs cross-validated smoothing parameter selection while the null distribution of the test is obtained via bootstrapping. The proposed approach allows applied researchers to test hypotheses concerning categorical variables in a fully nonparametric and robust framework, thereby deflecting potential criticism that a particular finding is driven by an arbitrary parametric specification. Simulations reveal that the test performs well, having significantly better power than a conventional frequency-based nonparametric test. The test is applied to determine whether OECD and non-OECD countries follow the same growth rate model or not. Our test suggests that OECD and non-OECD countries follow different growth rate models, while the tests based on a popular parametric specification and the conventional frequency-based nonparametric estimation method fail to detect any significant difference.

published proceedings


altmetric score

  • 3

author list (cited authors)

  • Racine, J. S., Hart, J., & Li, Q. i.

citation count

  • 83

complete list of authors

  • Racine, Jeffery S||Hart, Jeffrey||Li, Qi

publication date

  • January 2006