Revisiting the Location of FDI in China: A Panel Data Approach with Heterogeneous Shocks

Foreign Direct Investment (FDI) is viewed as a primary driving force in shaping the global economy and receives particular attention in empirical studies. In this paper, we argue that many of the existing studies ignore endogeneities that arise from shocks in source and destination countries. To address this endogeneity issue, we take the “controlling through estimating” idea from the econometric literature and propose using panel data models with heterogeneous shocks to deal with it. We consider the quasi maximum likelihood (QML) method to estimate our proposed model. We investigate the asymptotic properties of the QML estimator, including the consistency, the asymptotic representation, and the limiting distribution. We also propose new statistics to test the validity of the use of traditional dynamic and static panel data estimation methods. Applying it to the location determinants of inward FDI in China, we find that the endogeneity issue does exist, and that controlling for heterogeneous shocks helps to improve the estimation results.