A　semiparametric　regression　model　for　longitudinal　data　is　considered.　The　empirical　likelihood　method　is　used　to　estimate　the　regression　coefficients　and　the　baseline　function,　and　to　construct　confidence　regions　and　intervals.　It　is　proved　that　the　maximum　empirical　likelihood　estimator　of　the　regression　coefficients　achieves　asymptotic　efficiency　and　the　estimator　of　the　baseline　function　attains　asymptotic　normality　when　a　bias　correction　is　made.　Two　calibrated　empirical　likelihood　approaches　to　inference　for　the　baseline　function　are　developed.　We　propose　a　groupwise　empirical　likelihood　procedure　to　handle　the　inter-series　dependence　for　the　longitudinal　semiparametric　regression　model,　and　employ　bias　correction　to　construct　the　empirical　likelihood　ratio　functions　for　the　parameters　of　interest.　This　leads　us　to　prove　a　nonparametric　version　of　Wilks＇　theorem.　Compared　with　methods　based　on　normal　approximations,　the　empirical　likelihood　does　not　require　consistent　estimators　for　the　asymptotic　variance　and　bias.　A　simulation　compares　the　empirical　likelihood　and　normal-based　methods　in　terms　of　coverage　accuracies　and　average　areas/lengths　of　confidence　regions/intervals.