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Sequential Matching Estimation of Dynamic Causal Models
Lechner, Michael - 2021
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 … estimators perform well in small and medium size samples. Based on the application of the sequential matching estimators to an …
Persistent link: https://www.econbiz.de/10013319454
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Identification and estimation of causal effects of multiple treatments under the conditional independence assumption
Lechner, Michael - 1999
the approach retains its basic simplicity. The paper also outlines a matching estimator potentially suitable in that …
Persistent link: https://www.econbiz.de/10011317474
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Correlation and efficiency of propensity score-based estimators for average causal effects
Pingel, Ronnie; Waernbaum, Ingeborg - 2015
matching, inverse probability weighting and doubly robust estimators change under the case of correlated covariates …
Persistent link: https://www.econbiz.de/10010479992
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The estimation of multi-dimensional fixed effects panel data models
Balazsi, Laszlo; Mátyás, László; Wansbeek, Tom - 2015
The paper introduces the appropriate within estimators for the most frequently used three-dimensional fixed effects panel data models. It analyzes the behavior of these estimators in the cases of no self-flow data, unbalanced data, and dynamic autoregressive models. The main results are then...
Persistent link: https://www.econbiz.de/10010492323
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Nonparametric Regression Using Clusters
Vinod, Hrishikesh D. - 2017
We present a fundamentally unique method of nonparametric regression using clusters and test it against classically established methods. We compare two nonlinear regression estimation packages called ‘NNS', Viole (NNS: nonlinear nonparametric statistics, 2016), and ‘np', Hayfield and Racine...
Persistent link: https://www.econbiz.de/10012967797
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A Note on Regressions with Interval Data on a Regressor
Cerquera, Daniel; Laisney, Francois; Ullrich, Hannes - 2014
Motivated by Manski and Tamer (2002) and especially their partial identification analysis of the regression model where one covariate is only interval-measured, we present two extensions. Manski and Tamer (2002) propose two estimation approaches in this context, focussing on general results. The...
Persistent link: https://www.econbiz.de/10014141412
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A Note on Regressions with Interval Data on a Regressor
Cerquera, Daniel; Laisney, Francois; Ullrich, Hannes - 2015
We reconsider the partial identification analysis of the regression model in Manski and Tamer (2002) where one covariate is only interval-measured and present two additional sets of results. Manski and Tamer (2002) propose two estimation approaches in this context, focussing on general results....
Persistent link: https://www.econbiz.de/10014143561
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Dynamically Controlled Kernel Estimation
Kienitz, Joerg; Nowaczyk, Nikolai; Geng, Nancy(Qingxin) - 2021
We introduce a data driven and model free approach for computing conditional expectations. The new method is based on classical techniques combined with machine learning methods. In particular, we consider kernel density estimation based on simulated risk factors combined with a control variate....
Persistent link: https://www.econbiz.de/10013231705
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The Estimation of Multi-Dimensional Fixed Effects Panel Data Models
Balazsi, Laszlo - 2015
The paper introduces the appropriate within estimators for the most frequently used three-dimensional fixed effects panel data models. It analyzes the behavior of these estimators in the cases of no self-flow data, unbalanced data, and dynamic autoregressive models. The main results are then...
Persistent link: https://www.econbiz.de/10013024591
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Microeconometric evaluation of the active labour market policy in Switzerland
Gerfin, Michael; Lechner, Michael - 2000
and social security records. We apply a matching estimator adapted for the case of multiple programmes. We find …
Persistent link: https://www.econbiz.de/10011333282