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Sequential Matching Estimation of Dynamic Causal Models

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dc.creator Lechner, Michael
dc.date 2004
dc.date.accessioned 2013-10-16T07:09:42Z
dc.date.available 2013-10-16T07:09:42Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/20277
dc.identifier ppn:380765799
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/20277
dc.description This paper proposes sequential matching and inverse selection probability weighting to estimate dynamic causal effects. The sequential matching estimators extend simple, matching estimators based on propensity scores for static causal analysis that have been frequently applied in the evaluation literature. A Monte Carlo study shows that the suggested estimators perform well in small and medium size samples. Based on the application of the sequential matching estimators to an empirical problem - an evaluation study of the Swiss active labour market policies - some implementational issues are discussed and results are provided.
dc.language eng
dc.publisher
dc.relation IZA Discussion paper series 1042
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject C40
dc.subject ddc:330
dc.subject dynamic treatment effects
dc.subject nonparametric identification
dc.subject causal effects
dc.subject sequential randomisation
dc.subject programme evaluation
dc.subject panel data
dc.subject Kausalanalyse
dc.subject Schätztheorie
dc.subject Matching
dc.subject Nichtparametrisches Verfahren
dc.subject Arbeitsmarktpolitik
dc.subject Wirtschaftspolitische Wirkungsanalyse
dc.subject Theorie
dc.subject Schweiz
dc.title Sequential Matching Estimation of Dynamic Causal Models
dc.type doc-type:workingPaper


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