In　this　paper,　we　investigate　the　empirical　likelihood　inferences　of　varying　coefficient　errors-in-variables　models　with　longitudinal　data.　The　naive　empirical　log-likelihood　ratios　for　the　time-varying　coefficient　function　based　on　the　global　and　local　variance　structures　are　introduced.　The　corresponding　maximum　empirical　likelihood　estimators　of　the　time-varying　coefficients　are　derived,　and　their　asymptotic　properties　are　established.　Wilks＇　phenomenon　of　the　naive　empirical　log-likelihood　ratio,　which　ignores　the　within　subject　correlation,　is　proven　through　the　employment　of　undersmoothing.　To　avoid　the　undersmoothing,　we　recommend　a　residual-adjust　empirical　log-likelihood　ratio　and　prove　that　its　asymptotic　distribution　is　standard　chi-squared.　Thus,　this　result　can　be　used　to　construct　the　confidence　regions　of　the　time-varying　coefficients.　We　also　establish　the　asymptotic　distribution　theory　for　the　corresponding　residual-adjust　maximum　empirical　likelihood　estimator　and　find　it　to　be　unbiased　even　when　an　optimal　bandwidth　is　used.　Furthermore,　we　consider　the　construction　of　the　pointwise　confidence　interval　for　a　component　of　the　time-varying　coefficients　and　provide　the　simulation　studies　to　assess　the　finite　sample　performance,　while　we　conduct　a　real　example　to　illustrate　the　proposed　method.　(C)　2014　Elsevier　Inc.　All　rights　reserved.