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This article is concerned with the calibration of the empirical likelihood for semiparametric varying-coefficient partially linear models with diverging number of parameters. However, there is always substantial lack-of-fit, when the empirical likelihood ratio is calibrated by a bias-corrected empirical likelihood, producing tests with type I errors much larger than nominal levels. So we consider an effective calibration method and study the asymptotic behavior of this bias-corrected empirical likelihood ratio function. Some simulation studies are conducted to illustrate our approach.

Calibration of the empirical likelihood for semiparametric varying-coefficient partially linear models with diverging number of parameters