Testing the significance of categorical predictor variables in nonparametric regression models’, Econometric Reviews 25

Abstract. In this paper we propose a test for the signi¯cance 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 de°ecting potential criticism that a particular ¯nding is driven by an arbitrary parametric speci¯cation. Simulations reveal that the test performs well, having signi¯cantly 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 di®erent growth rate models, while the tests based on a popular parametric speci¯cation and the conventional frequency-based nonparametric estimation method fail to detect any signi¯cant di®erence. Date: July 22, 2004. The authors would like to thank but not implicate Essie Maasoumi and Mike Veall for their helpful comments. A preliminary draft of this paper was presented at the 2002 International Conference on Current Advances and Trends in Nonparametric Statistics held in Crete, and we would like to than