标题：A Monte Carlo Analysis of Robustness of the Synthetic Control Method and Dynamic Panel Estimation: A Comparative Case Study of a Policy Intervention.
期刊名称：Journal of Statistical and Econometric Methods
印刷版ISSN：2241-0384
电子版ISSN：2241-0376
出版年度：2020
卷号：9
期号：1
页码：67-87
语种：English
出版社：Scienpress Ltd
摘要：In comparative case studies, by solving an optimization problem, the syntheticcontrol method provides a point estimate for an intervention effect and it suffersfrom lack of considering an asymptotic distribution of the estimator. On the otherhand, we can benefit from such considerations while working with a regressionframework; and many studies have been done and many methods have been offeredin order to overcome the potential shortages of a traditional regression frameworkin such case studies. In this paper, we use Monte Carlo simulation to compare therobustness and sensitivity between the synthetic control method and a dynamicpanel data regression framework. Empirical work in based on a suitable case of apolicy intervention and a comparative case study: sanctions on Iran. We concludethat the dynamic panel data model seems to be performing well with the macro levelaggregate data and a comparative case study scenario, and the assumptions areappropriate. However, for the synthetic control method we observe large standarderrors in the estimated values which result in insignificance of the point estimates.We also take advantage of the replicated trials, and we analyze and compare thesensitivity of the synthetic control method and the dynamic panel data model to thechoice of the donor pool and the treatment assignment.
关键词：Synthetic Control Method; Panel Data Model; Monte CarloSimulation; Comparison
