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Testing the significance of categorical predictor variables in nonparametric regression models. (English) Zbl 1106.62046
Summary: 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.

MSC:
62G08 Nonparametric regression and quantile regression
62G10 Nonparametric hypothesis testing
65C05 Monte Carlo methods
62P20 Applications of statistics to economics
62G09 Nonparametric statistical resampling methods
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