In　this　paper,　we　focus　on　the　variable　selection　for　semiparametric　varying　coefficient　partially　linear　models　with　longitudinal　data.　A　new　variable　selection　procedure　is　proposed　based　on　the　combination　of　the　basis　function　approximations　and　quadratic　inference　functions.　The　proposed　procedure　simultaneously　selects　significant　variables　in　the　parametric　components　and　the　nonparametric　components.　With　appropriate　selection　of　the　tuning　parameters,　we　establish　the　consistency　and　asymptotic　normality　of　the　resulting　estimators.　Extensive　Monte　Carlo　simulation　studies　are　conducted　to　examine　the　finite　sample　performance　of　the　proposed　variable　selection　procedure.　We　further　illustrate　the　proposed　procedure　by　an　application.　(C)　2014　Elsevier　Inc.　All　rights　reserved.