In　this　article,　the　generalized　linear　model　for　longitudinal　data　is　studied.　A　generalized　empirical　likelihood　method　is　proposed　by　combining　generalized　estimating　equations　and　quadratic　inference　functions　based　on　the　working　correlation　matrix.　It　is　proved　that　the　proposed　generalized　empirical　likelihood　ratios　are　asymptotically　chi-squared　under　some　suitable　conditions,　and　hence　it　can　be　used　to　construct　the　confidence　regions　of　the　parameters.　In　addition,　the　maximum　empirical　likelihood　estimates　of　parameters　are　obtained,　and　their　asymptotic　normalities　are　proved.　Some　simulations　are　undertaken　to　compare　the　generalized　empirical　likelihood　and　normal　approximation-based　method　in　terms　of　coverage　accuracies　and　average　areas/lengths　of　confidence　regions/intervals.　An　example　of　a　real　data　is　used　for　illustrating　our　methods.