In　this　paper,　we　develop　statistical　inference　techniques　for　the　unknown　coefficient　functions　and　single-index　parameters　in　single-index　varying-coefficient　models.　We　first　estimate　the　nonparametric　component　via　the　local　linear　fitting,　then　construct　an　estimated　empirical　likelihood　ratio　function　and　hence　obtain　a　maximum　empirical　likelihood　estimator　for　the　parametric　component.　Our　estimator　for　parametric　component　is　asymptotically　efficient,　and　the　estimator　of　nonparametric　component　has　an　optimal　convergence　rate.　Our　results　provide　ways　to　construct　the　confidence　region　for　the　involved　unknown　parameter.　We　also　develop　an　adjusted　empirical　likelihood　ratio　for　constructing　the　confidence　regions　of　parameters　of　interest.　A　simulation　study　is　conducted　to　evaluate　the　finite　sample　behaviors　of　the　proposed　methods.