Can a time-varying structure provide a more robust panel construction of counterfactuals-straitjacket or straitjackets?

Measuring treatment effects are a complicated task as the outcomes of receiving and not receiving the treatment cannot be observed simultaneously. Thus, the issue of obtaining accurate measurement is an issue of predicting the counterfactuals accurately. In this study, we explore the suitability of using time-varying parameter models in a panel to generate robust measure of counterfactuals, hence robust measure of treatment effects. We suggest some within-sample tests for constant parameter versus time-varying parameter models and diagnostic tools based on time-varying parameter framework as a flexible alternative to predict missing data. Monte Carlos and two empirical studies are examined in this framework. The results appear to show that if the focus is on minimizing the mean square error of the predicted treatment effects, a “straitjacket” approach relying on the best selected model from the pre-treatment data remains the best option in view of the missing post-treatment information on counterfactuals. On the other hand, the confidence band based on the time-varying parameter model provides a more robust inference to hedge against possible changes of the relations between the treated units and the controls in the post-treatment period.