Showing 31 - 40 of 62,939
In this paper nonparametric instrumental variable estimation of local average treatment effects (LATE) is extended to incorporate confounding covariates. Estimation of local average treatment effects is appealing since their identification relies on much weaker assumptions than the...
Persistent link: https://www.econbiz.de/10005453938
Propensity score matching is a nonparametric technique frequently used for estimating average treatment effects. Yet … its applicability is not confined to treatment evaluation. In this paper the propensity score property is generalized to … the setting of selection on unobservables. It is shown that propensity score matching can be used to decompose effects due …
Persistent link: https://www.econbiz.de/10005453940
This paper reviews the main identification and estimation strategies for microeconometric policy evaluation. Particular … nonparametric matching and weighting estimators of the average treatment effects and their properties are examined. …
Persistent link: https://www.econbiz.de/10005696723
This paper reviews the main identification and estimation strategies for microeconomic policy evaluation. Particular … nonparametric matching and weighting estimators of the average treatment effects and their properties are examined. …
Persistent link: https://www.econbiz.de/10005566371
In this paper nonparametric instrumental variable estimation of local average treatment effects (LATE) is extended to incorporate confounding covariates. Estimation of local average treatment effects is appealing since their identification relies on much weaker assumptions than the...
Persistent link: https://www.econbiz.de/10005566385
Hahn [Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66:315-331] derived the semiparametric efficiency bounds for estimating the average treatment effect (ATE) and the average treatment effect on the treated...
Persistent link: https://www.econbiz.de/10009279879
This paper proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the...
Persistent link: https://www.econbiz.de/10011786988
This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design (RDD) and proposes simple estimators. Quantile treatment effects are a very helpful tool to characterize the effects of certain interventions on the outcome distribution. The...
Persistent link: https://www.econbiz.de/10005233749
In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension...
Persistent link: https://www.econbiz.de/10005200689
In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension...
Persistent link: https://www.econbiz.de/10005762088