In　this　article,　empirical　likelihood　inferences　for　semiparametric　varying-coefficient　partially　linear　models　with　longitudinal　data　are　investigated.　We　propose　a　groupwise　empirical　likelihood　procedure　to　handle　the　inter-series　dependence　of　the　longitudinal　data.　By　using　residual-adjustment,　an　empirical　likelihood　ratio　function　for　the　nonparametric　component　is　constructed,　and　a　nonparametric　version　Wilks＇　phenomenons　is　proved.　Compared　with　methods　based　on　normal　approximations,　the　empirical　likelihood　does　not　require　consistent　estimators　for　the　asymptotic　variance　and　bias.　A　simulation　study　is　undertaken　to　assess　the　finite　sample　performance　of　the　proposed　confidence　regions.