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Difference-in-differences (DID) is commonly used for causal inference in time-series cross-section data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, I propose a method that not...
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-- Experiments -- Matching -- Instrumental Variables -- Regression Discontinuity Design -- Panel Data and fixed effects -- Difference …, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta …
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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
Across many disciplines, the fixed effects estimator of linear panel data models is the default method to estimate … panel. We consider several alternatives to the fixed effects estimator with T > 2 when relevant unit-specific heterogeneity … results from multiple linear panel data estimators in applied research. …
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We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always …
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We consider inference about coefficients on a small number of variables of interest in a linear panel data model with …
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