The　empirical　likelihood　method　is　especially　useful　for　constructing　confidence　intervals　or　regions　of　parameters　of　interest.　Yet,　the　technique　cannot　be　directly　applied　to　partially　linear　single-index　models　for　longitudinal　data　due　to　the　within-subject　correlation.　In　this　paper,　a　bias-corrected　block　empirical　likelihood　(BCBEL)　method　is　suggested　to　study　the　models　by　accounting　for　the　within-subject　correlation.　BCBEL　shares　some　desired　features:　unlike　any　normal　approximation　based　method　for　confidence　region,　the　estimation　of　parameters　with　the　iterative　algorithm　is　avoided　and　a　consistent　estimator　of　the　asymptotic　covariance　matrix　is　not　needed.　Because　of　bias　correction,　the　BCBEL　ratio　is　asymptotically　chi-squared,　and　hence　it　can　be　directly　used　to　construct　confidence　regions　of　the　parameters　without　any　extra　Monte　Carlo　approximation　that　is　needed　when　bias　correction　is　not　applied.　The　proposed　method　can　naturally　be　applied　to　deal　with　pure　single-index　models　and　partially　linear　models　for　longitudinal　data.　Some　simulation　studies　are　carried　out　and　an　example　in　epidemiology　is　given　for　illustration.　(C)　2009　Elsevier　Inc.　All　rights　reserved.