显而易见，全局变量可以做同样的工作，但它们带来了大家熟知的全局性名称空间污染的问题，并会因非局部性而引起错误。

Obviously, global variables could do the same job, but they cause the familiar problems with pollution of the global namespace, and allow mistakes due to non-locality.

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本文基于非参数回归模型的局部核权最小二乘法提出变量间非线性协整的一种非参数检验方法。

Based on the locally kernel weighted least squares fit of the nonparametric regression models, this paper presents the nonparametric testing method for nonlinear cointegration.

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本文基于非参数可加回归模型的局部核权最小二乘法提出变量间非线性协整的一种非参数检验方法。

Based on the local kernal weighted least squared fit of the nonparametric and additive regression model, this paper presents the nonparametric testing method for nonlinear cointegration.

youdao

本文基于非参数可加回归模型的局部核权最小二乘法提出变量间非线性协整的一种非参数检验方法。

Based on the local kernal weighted least squared fit of the nonparametric and additive regression model, this paper presents the nonparametric testing method for nonlinear cointegration.

youdao