We　consider　the　problem　of　variable　selection　for　the　generalized　linear　models　(GLMs)　with　longitudinal　data.　An　automatic　variable　selection　procedure　is　developed　using　smooth-threshold　generalized　estimating　equations　(SGEE).　The　proposed　procedure　automatically　eliminates　inactive　predictors　by　setting　the　corresponding　parameters　to　be　zero,　and　simultaneously　estimates　the　nonzero　regression　coefficients　by　solving　the　SGEE.　The　proposed　method　shares　some　of　the　desired　features　of　existing　variable　selection　methods:　the　resulting　estimator　enjoys　the　oracle　property;　the　proposed　procedure　avoids　the　convex　optimization　problem　and　is　flexible　and　easy　to　implement.　Moreover,　we　propose　a　penalized　weighted　deviance　criterion　for　a　data-driven　choice　of　the　tuning　parameters.　Simulation　studies　are　carried　out　to　assess　the　performance　of　SGEE,　and　a　real　dataset　is　analyzed　for　further　illustration.　(C)　2012　Elsevier　B.V.　All　rights　reserved.