Date of Degree
Econometrics | Growth and Development | International Economics
Synthetic Control, Dynamic Panel Data, GDP
International sanctions imposed on Iran, targeting primarily Iran’s key energy sector and its ability to access the international ﬁnancial system, have harmed Iran’s economic growth, speciﬁcally from 2011 to 2014. This thesis uses this case to study and compare the applicability of two diﬀerent popular approaches used in comparative case studies exploring the eﬀect of a policy intervention.
In the Chapter 1 we study the synthetic control method. Using this method, we estimate the eﬀect of the intensiﬁcation of sanctions on Iran’s GDP during the period 2011 to 2014. The year of 2011 was Iran’s ﬁrst full year under these heavy sanctions, and in 2015, the Iran nuclear deal framework was established. Prior to this time, in spite of the ongoing U.S. sanctions, Iran’s GDP had a positive trend from 1990 to 2011. However, our estimates show that the GDP suﬀered a hit of more than 17 percent over the period under question. We ﬁnd that these eﬀects were particularly severe in 2012 – the same year of the enforcement by the European Union of an oil embargo and added ﬁnancial boycotts against Iran.
In Chapter 2, we take a diﬀerent approach to the same case, and incorporate a more structural and traditional framework. We use a Diﬀerence-in-Diﬀerence model as well as a dynamic panel data model to estimate the eﬀect of sanctions. According to the dynamic panel data estimation, the cumulative eﬀect of sanctions on the country’s GDP is −11.40,−18.12, and −18.62 percent for 2012, 2013, and 2014. In this chapter, we also compare the synthetic control method with the dynamic panel data regression framework. First, we show that the synthetic control method provides an unbiased estimator if the underlying model of the outcome variable of interest is a dynamic panel data model. Second, we compare the prediction power of these two methods.
In Chapter 3 we design a Monte Carlo study to discuss the performance of the methods used in previous chapters over many replications. In this chapter, we examine the robustness of the method. We conclude that the dynamic panel data model seems to be performing well with the macro level aggregate data, and the assumptions are appropriate. However, for the synthetic control method we observe large standard error in the estimated values. If we translate that to a signiﬁcance analysis, this means that even though we observe meaningful values reported as the eﬀect of the intervention, they are not statistically signiﬁcantly diﬀerent from 0.
Gharehgozli, Orkideh, "Synthetic Control and Dynamic Panel Estimation: A Case Study of Iran" (2018). CUNY Academic Works.