A nonparametric hypothesis test via the Bootstrap resampling

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.

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Year of publication:	2011-06-28
Authors:	Temel, Tugrul
Institutions:	Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München
Subject:	Hypothesis test \| the bootstrap \| nonparametric regression \| omitted variables

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Extent:	application/pdf
Series:	MPRA Paper.
Type of publication:	Book / Working Paper
Classification:	C14 - Semiparametric and Nonparametric Methods ; C12 - Hypothesis Testing ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009148028