▁recommended . ▁ ▁No ▁aut oc or relation : ▁the ▁errors ▁are ▁un cor related ▁between ▁observations : ▁ ▁for ▁. ▁This ▁assumption ▁may ▁be ▁viol ated ▁in ▁the ▁context ▁of ▁time ▁series ▁data , ▁panel ▁data , ▁cluster ▁samples , ▁hier arch ical ▁data , ▁repeated ▁measures ▁data , ▁longitud inal ▁data , ▁and ▁other ▁data ▁with ▁dependencies . ▁In ▁such ▁cases ▁generalized ▁least ▁squares ▁provides ▁a ▁better ▁alternative ▁than ▁the ▁O LS . ▁Another ▁expression ▁for ▁aut oc or relation ▁is ▁serial ▁correlation . ▁▁ ▁Normal ity . ▁It ▁is ▁sometimes ▁addition ally ▁assumed ▁that ▁the ▁errors ▁have ▁normal ▁distribution ▁conditional ▁on ▁the ▁re gress ors : ▁▁ ▁This ▁assumption ▁is ▁not ▁needed ▁for ▁the ▁valid ity ▁of ▁the ▁O LS ▁method , ▁although ▁certain ▁additional ▁finite - sample ▁properties ▁can ▁be ▁established ▁in ▁case ▁when ▁it ▁does ▁( es pecially ▁in ▁the ▁area ▁of ▁hypoth eses ▁testing ). ▁Also ▁when ▁the ▁errors ▁are ▁normal , ▁the ▁O LS ▁estim ator ▁is ▁equivalent ▁to ▁the ▁maximum ▁lik elihood ▁estim ator ▁( M LE ), ▁and ▁therefore ▁it ▁is ▁asympt ot ically ▁efficient ▁in ▁the ▁class ▁of ▁all ▁regular ▁estim ators . ▁Import antly , ▁the ▁normal ity ▁assumption ▁applies ▁only ▁to ▁the ▁error ▁terms ; ▁contrary ▁to ▁a ▁popular ▁mis con ception , ▁the ▁response ▁( dependent ) ▁variable ▁is ▁not ▁required ▁to ▁be ▁normally ▁distributed . ▁▁ ▁Independent ▁and ▁ident ically ▁distributed ▁( i id ) ▁ ▁In ▁some ▁applications , ▁especially ▁with ▁cross - section al ▁data , ▁an