This　paper　focuses　on　variable　selections　for　varying　coefficient　models　when　some　covariates　are　measured　with　errors.　We　present　a　bias-corrected　variable　selection　procedure　by　combining　basis　function　approximations　with　shrinkage　estimations.　With　appropriate　selection　of　the　tuning　parameters,　we　establish　the　consistency　of　the　variable　selection　procedure,　and　derive　the　optimal　convergence　rate　of　the　regularized　estimators.　A　simulation　study　and　a　real　data　application　are　undertaken　to　assess　the　finite　sample　performance　of　the　proposed　variable　selection　procedure.