<p>In this paper, we propose a model identification and selection method for varying coefficient errors-in-variables (EV) models with missing responses, termed the imputation-based bias-corrected double-penalized estimating equation (ibbcDPEE) method. The proposed method does not need to assume in advance whether the regression coefficients in models are constants or varying coefficients. First, it utilizes B-spline basis functions to approximate the nonparametric regression coefficients. Subsequently, the bias-corrected double-penalized estimating equation (bcDPEE) is constructed based on the observed responses, while accounting for the bias in the unobserved covariates. The missing responses are then imputed via the kernel estimation technique. Lastly, the ibbcDPEE is constructed to do model identification and selection simultaneously. Under some regularity conditions, the proposed method can consistently identify and select varying coefficients and nonzero constant coefficients. Moreover, the estimators of the varying coefficients achieve the optimal convergence rate of nonparametric function estimation. The finite sample performance of the proposed method is evaluated through simulation studies and a real data analysis.</p>