Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/20277 
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dc.contributor.authorLechner, Michaelen
dc.date.accessioned2009-01-28T16:12:56Z-
dc.date.available2009-01-28T16:12:56Z-
dc.date.issued2004-
dc.identifier.urihttp://hdl.handle.net/10419/20277-
dc.description.abstractThis paper proposes sequential matching and inverse selection probability weighting toestimate dynamic causal effects. The sequential matching estimators extend simple,matching estimators based on propensity scores for static causal analysis that have beenfrequently applied in the evaluation literature. A Monte Carlo study shows that the suggestedestimators perform well in small and medium size samples. Based on the application of thesequential matching estimators to an empirical problem - an evaluation study of the Swissactive labour market policies - some implementational issues are discussed and results areprovided.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x1042en
dc.subject.jelC40en
dc.subject.ddc330en
dc.subject.keyworddynamic treatment effectsen
dc.subject.keywordnonparametric identificationen
dc.subject.keywordcausal effectsen
dc.subject.keywordsequential randomisationen
dc.subject.keywordprogramme evaluationen
dc.subject.keywordpanel dataen
dc.subject.stwKausalanalyseen
dc.subject.stwSchätztheorieen
dc.subject.stwMatchingen
dc.subject.stwNichtparametrisches Verfahrenen
dc.subject.stwArbeitsmarktpolitiken
dc.subject.stwWirtschaftspolitische Wirkungsanalyseen
dc.subject.stwTheorieen
dc.subject.stwSchweizen
dc.titleSequential Matching Estimation of Dynamic Causal Models-
dc.typeWorking Paperen
dc.identifier.ppn380765799en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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