Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models |
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Authors: | Jeffery S Racine Jeffrey Hart Qi Li |
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Institution: |
a Department of Economics, McMaster University, Hamilton, Ontario, Canada
b Department of Statistics, Texas A& M University, College Station, Texas, USA
c Department of Economics, Texas A& M University, College Station, Texas, USA
d Tsinghua University, Beijing, China |
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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. |
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Keywords: | Discrete regressors Inference Kernel smoothing |
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