![]() We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation. ![]() Under this framework, we propose two sets of diagnostic tests, tests for (no) pre-trend and placebo tests, accompanied by visualization tools, to help researchers gauge the validity of the no-time-varying-confounder assumption. These estimators provide more reliable causal estimates than conventional two-way fixed effects models when the treatment effects are heterogeneous or unobserved time-varying confounders exist. Its special cases include several newly developed methods, such as the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. Let us try, with the QoG institutes time series cross section dataset, which contains information about countries, over time. We simply type xtset country year - the panel variable first, and then the time variable. This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. The command to specify these variables is xtset.
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