Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/20277
Authors: 
Lechner, Michael
Year of Publication: 
2004
Series/Report no.: 
IZA Discussion paper series 1042
Abstract: 
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.
Subjects: 
dynamic treatment effects
nonparametric identification
causal effects
sequential randomisation
programme evaluation
panel data
JEL: 
C40
Document Type: 
Working Paper

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