In　this　paper,　the　empirical　likelihood-based　inference　is　investigated　with　varying　coefficient　panel　data　models　with　fixed　effect.　A　naive　empirical　likelihood　ratio　is　firstly　proposed　after　the　fixed　effect　is　corrected.　The　maximum　empirical　likelihood　estimators　for　the　coefficient　functions　are　derived　as　well　as　their　asymptotic　properties.　Wilk＇s　phenomenon　of　this　naive　empirical　likelihood　ratio　is　proven　under　a　undersmoothing　assumption.　To　avoid　the　requisition　of　undersmoothing　and　perform　an　efficient　inference,　a　residual-adjusted　empirical　likelihood　ratio　is　further　suggested　and　shown　as　having　a　standard　chi-square　limit　distribution,　by　which　the　confidence　regions　of　the　coefficient　functions　are　constructed.　Another　estimators　for　the　coefficient　functions,　together　with　their　asymptotic　properties,　are　considered　by　maximizing　the　residual-adjusted　empirical　log-likelihood　function　under　an　optimal　bandwidth.　The　performances　of　these　proposed　estimators　and　confidence　regions　are　assessed　through　numerical　simulations　and　a　real　data　analysis.