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Abstract

This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.



Item Type: MPRA Paper -

Original Title: A nonparametric hypothesis test via the Bootstrap resampling-

Language: English-

Keywords: Hypothesis test; the bootstrap; nonparametric regression; omitted variables-

Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: GeneralC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: GeneralC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General-





Author: Temel, Tugrul

Source: https://mpra.ub.uni-muenchen.de/31880/







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