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Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models

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Author Info

  • Jeffery Racine
  • Jeffrey Hart
  • Qi Li

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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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 25 (2006)
Issue (Month): 4 ()
Pages: 523-544

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Handle: RePEc:taf:emetrv:v:25:y:2006:i:4:p:523-544

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Related research

Keywords: Discrete regressors; Inference; Kernel smoothing;

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