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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
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Moving the Goalposts : Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand
Crump, Richard K.; Hotz, Vincent Joseph; Imbens, Guido; … - 2021
Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by …
Persistent link: https://www.econbiz.de/10013317404
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Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium
Verardi, Vincenzo - 2010
estimation of the popular linear fixed effects panel data model, and to supply Stata code for it. In an application from the …
Persistent link: https://www.econbiz.de/10013143679
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Infections, Accidents and Nursing Overtime in a Neonatal Intensive Care Unit : A Bayesian Semiparametric Panel Data Logit Model
Beltempo, Marc - 2020
using a sample of 3,979 neonates which represents over 84,846 observations (infant/days).We use a semiparametric panel data …
Persistent link: https://www.econbiz.de/10012838501
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Regression Discontinuity Design with Continuous Measurement Error in the Running Variable
Davezies, Laurent - 2017
Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent...
Persistent link: https://www.econbiz.de/10012955015
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Machine Learning Estimation of Heterogeneous Causal Effects : Empirical Monte Carlo Evidence
Knaus, Michael C. - 2019
We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...
Persistent link: https://www.econbiz.de/10012894534
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Immigrant Heterogeneity and the Earnings Distribution in the United Kingdom and United States : New Evidence from a Panel Data Quantile Regression Analysis
Billger, Sherrilyn M. - 2010
In this paper we use a relatively new panel data quantile regression technique to examine native-immigrant earnings …
Persistent link: https://www.econbiz.de/10013136720
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The Exporter Productivity Premium Along the Productivity Distribution : First Evidence from a Quantile Regression Approach for Fixed Effects Panel Data Models
Powell, David - 2010
An emerging literature on international activities of heterogeneous firms documents that exporting firms are more productive than firms that only sell on the national market. This positive exporter productivity premium shows up in a large number of empirical studies after controlling for...
Persistent link: https://www.econbiz.de/10013139057
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Diagnostic Tests for Homoskedasticity in Spatial Cross-Sectional or Panel Models
Baltagi, Badi H.; Pirotte, Alain; Yang, Zhenlin - 2021
three representative models: spatial cross-sectional, static or dynamic panel models. Monte Carlo results show that the …
Persistent link: https://www.econbiz.de/10013315158
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Difference‐In‐Difference Estimation by FE and OLS When There is Panel Non‐Response
Lechner, Michael - 2015
there is time varying panel non-response. If such non-response does not affect the common-trend assumption, then OLS and FE … estimation may still be consistent, while OLS will be inconsistent. We provide simulation as well as empirical evidence for this … evidence that non‐response is not ignorable for the differences-in-differences estimation …
Persistent link: https://www.econbiz.de/10013012023