Testing for a constant coefficient of variation in nonparametric regression
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Date
2007-12-04T14:07:21Z
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Abstract
In this paper we propose a new test for the hypothesis of a constant coefficient of variation in the common nonparametric regression model. The test is based on an estimate of the L2- distance between the square of the regression function and variance function. We prove
asymptotic normality of a standardized estimate of this distance under the null hypothesis
and fixed alternatives and the finite sample properties of a corresponding bootstrap test
are investigated by means of a simulation study. The results are applicable to stationary
processes with the common mixing conditions and are used to construct tests for ARCH
assumptions in financial time series.
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Keywords
Constant cofficient of variation, Generalized nonparametric regression models, Multiplicative error structure, Nonparametric regression, Stationary processes