A　three-stage　computation　framework　for　solving　parameter　estimation　problems　for　dynamic　systems　with　multiple　data　profiles　is　developed.　The　dynamic　parameter　estimation　problem　is　transformed　into　a　nonlinear　programming　(NLP)　problem　by　using　collocation　on　finite　elements.　The　model　parameters　to　be　estimated　are　treated　in　the　upper　stage　by　solving　an　NLP　problem.　The　middle　stage　consists　of　multiple　NLP　problems　nested　in　the　upper　stage,　representing　the　data　reconciliation　step　for　each　data　profile.　We　use　the　quasi-sequential　dynamic　optimization　approach　to　solve　these　problems.　In　the　lower　stage,　the　state　variables　and　their　gradients　are　evaluated　through　integrating　the　model　equations.　Since　the　second-order　derivatives　are　not　required　in　the　computation　framework　this　proposed　method　will　be　efficient　for　solving　nonlinear　dynamic　parameter　estimation　problems.　The　computational　results　obtained　on　a　parameter　estimation　problem　for　two　CSTR　models　demonstrate　the　effectiveness　of　the　proposed　approach.　©　2013　Korean　Institute　of　Chemical　Engineers,　Seoul,　Korea.