In　this　paper,　we　propose　a　new　criterion,　named　PICa,　to　simultaneously　select　explanatory　variables　in　the　mean　model　and　variance　model　in　heteroscedastic　linear　models　based　on　the　model　structure.　We　show　that　the　new　criterion　can　select　the　true　mean　model　and　a　correct　variance　model　with　probability　tending　to　1　under　mild　conditions.　Simulation　studies　and　a　real　example　are　presented　to　evaluate　the　new　criterion,　and　it　turns　out　that　the　proposed　approach　performs　well.