Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models

Jeffrey Racine, Jeffrey Hart and Qi Li

Econometric Reviews, 2006, vol. 25, issue 4, 523-544

Abstract: 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.

Keywords: Discrete regressors; Inference; Kernel smoothing (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (104)

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DOI: 10.1080/07474930600972590

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