Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/20277
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dc.creatorLechner, Michael-
dc.date2004-
dc.date.accessioned2013-10-16T07:09:42Z-
dc.date.available2013-10-16T07:09:42Z-
dc.date.issued2013-10-16-
dc.identifierhttp://hdl.handle.net/10419/20277-
dc.identifierppn:380765799-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/10419/20277-
dc.descriptionThis 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.languageeng-
dc.publisher-
dc.relationIZA Discussion paper series 1042-
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.subjectC40-
dc.subjectddc:330-
dc.subjectdynamic treatment effects-
dc.subjectnonparametric identification-
dc.subjectcausal effects-
dc.subjectsequential randomisation-
dc.subjectprogramme evaluation-
dc.subjectpanel data-
dc.subjectKausalanalyse-
dc.subjectSchätztheorie-
dc.subjectMatching-
dc.subjectNichtparametrisches Verfahren-
dc.subjectArbeitsmarktpolitik-
dc.subjectWirtschaftspolitische Wirkungsanalyse-
dc.subjectTheorie-
dc.subjectSchweiz-
dc.titleSequential Matching Estimation of Dynamic Causal Models-
dc.typedoc-type:workingPaper-
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