This　paper　studies　tools　for　checking　the　validity　of　a　parametric　regression　model,　when　both　response　and　predictors　are　unobserved　and　distorted　in　a　multiplicative　fashion　by　an　observed　confounding　variable.　A　residual　based　empirical　process　test　statistic　marked　by　proper　functions　of　the　regressors　is　proposed.　We　derive　asymptotic　distribution　of　the　proposed　empirical　process　test　statistic:　a　centered　Gaussian　process　under　the　null　hypothesis　and　a　non-centered　one　under　local　alternatives　converging　to　the　null　hypothesis　at　parametric　rates.　We　also　suggest　a　bootstrap　procedure　to　calculate　critical　values.　Simulation　studies　are　conducted　to　demonstrate　the　performance　of　the　proposed　test　statistic　and　real　examples　are　analyzed　for　illustrations.　(C)　2014　Elsevier　B.V.　All　rights　reserved.