Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models

Heteroscedasticity diagnostics of error variance is essential before performing some statistical inference work. This paper is concerned with the statistical diagnostics for the varying coefficient partially nonlinear model. We propose a novel diagnostic approach for heteroscedasticity of error variance in the model by combining it with the empirical likelihood method. Under some mild conditions, the nonparametric version of the Wilks theorem is obtained. Furthermore, simulation studies and a real data analysis are implemented to evaluate the performances of our proposed approaches.