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Treatment evaluation with multiple outcome periods under endogeneity and attrition

Frölich, Markus; Huber, Martin - 2014

This paper develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pre-treatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment...

Persistent link: https://www.econbiz.de/10010249397

A unified framework for dynamic treatment effect estimation in interactive fixed effect models

Brown, Nicholas; Butts, Kyle - 2022

We present a unifying identification strategy of dynamic average treatment effect parameters for staggered interventions when parallel trends are valid only after controlling for interactive fixed effects. This setting nests the usual parallel trends assumption, but allows treated units to have...

Persistent link: https://www.econbiz.de/10013556783

Trimmed mean group estimation of average treatment effects in ultra short T panels under correlated heterogeneity

Pesaran, M. Hashem; Yang, Liying - 2023

if the time dimension of the panel is as small as the number of its regressors. Extensions to panels with time effects …

Persistent link: https://www.econbiz.de/10014393231

Pitfalls when estimating treatment effects using clustered data

MacKinnon, James G.; Webb, Matthew - 2017

Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard...

Persistent link: https://www.econbiz.de/10011722291

Quantile treatment effects in difference in differences models with panel data

Callaway, Brantly; Li, Tong - In: Quantitative economics : QE ; journal of the … 10 (2019) 4, pp. 1579-1618

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a … Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available …

Persistent link: https://www.econbiz.de/10012202873

Efficient Propensity Score Regression Estimators of Multivalued Treatment Effects for the Treated

Lee, Ying-Ying - 2017

Matching is a widely used program evaluation estimation method when treatment is assigned at random conditional on … are unknown and when they are known. We derive the large sample distribution that accounts for the estimation error of the …

Persistent link: https://www.econbiz.de/10012964498

Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed

Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - 2020

. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation … and conventional estimation becomes more similar in larger samples and higher treatment shares. …

Persistent link: https://www.econbiz.de/10012165548

Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed

Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - 2019

Persistent link: https://www.econbiz.de/10012060603

New Evidence on Linear Regression and Treatment Effect Heterogeneity

Słoczyński, Tymon - 2015

It is standard practice in applied work to rely on linear least squares regression to estimate the effect of a binary variable ("treatment") on some outcome of interest. In this paper I study the interpretation of the regression estimand when treatment effects are in fact heterogeneous.I show...

Persistent link: https://www.econbiz.de/10013012020

Quasi ML Estimation of the Panel AR(1) Model with Additional Regressors

Kruiniger, Hugo - 2019

heteroskedasticity and are extensions and generalizations of the models considered in Kruiniger (2013. Quasi ML estimation of the panel … Quasi ML estimators (MLEs) for panel AR(1) models with additional regressors. We also consider related GMM estimators. All … individual effects; and we discuss estimation of models with time-varying individual effects. We also discuss how to choose …

Persistent link: https://www.econbiz.de/10012903818