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What is the value of knowing the propensity score for estimating average treatment effects?

Froelich, Markus - School of Economics and Political Science, Universität … - 2002

Propensity score matching is widely used in treatment evaluation to estimate average treatment effects. Nevertheless …

Persistent link: https://www.econbiz.de/10005797660

Nonparametric IV estimation of local average treatment effects with covariates

Froelich, Markus - School of Economics and Political Science, Universität … - 2002

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

A generalization of the balancing property of the propensity score

Froelich, Markus - School of Economics and Political Science, Universität … - 2002

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

Programme Evaluation with Multiple Treatments

Froelich, Markus - School of Economics and Political Science, Universität … - 2002

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

Regression discontinuity design with covariates

Frölich, Markus - School of Economics and Political Science, Universität … - 2007

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

A Note on the Relation of Weighting and Matching Estimators

Lechner, Michael - School of Economics and Political Science, Universität … - 2007

This paper compares the inverse-probability-of-selection-weighting estimation principle with the matching principle and … derives conditions for weighting and matching to identify the same and the true distribution, respectively. This comparison …

Persistent link: https://www.econbiz.de/10005200691

Efficient estimation of the semiparametric spatial autoregressive model

Robinson, Peter M. - London School of Economics (LSE) - 2007

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series...

Persistent link: https://www.econbiz.de/10010928599

Nonparametric estimation with aggregated data

Linton, Oliver; Whang, Yoon-Jae - London School of Economics (LSE) - 2000

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from different families be in dependent. We establish consistency and asymptotic...

Persistent link: https://www.econbiz.de/10010928627

A quantilogram approach to evaluating directional predictability

Linton, Oliver; Whang, Yoon-Jae - London School of Economics (LSE) - 2003

In this note we propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on the correlogram of quantile hits. We provide the distribution theory needed to conduct inference,...

Persistent link: https://www.econbiz.de/10010928727

Semiparametric regression analysis under imputation for missing response data

Hardle, Wolfgang; Linton, Oliver; Wang, Qihua - London School of Economics (LSE) - 2003

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator, a marginal average estimator and a (marginal) propensity score weighted estimator are defined. All the estimators are proved to be asymptotically normal,...

Persistent link: https://www.econbiz.de/10010928736

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