Robust FDI Determinants: Bayesian Model Averaging In The Presence Of Selection Bias
The literature on Foreign Direct Investment (FDI) determinants is remarkably diverse in terms of competing theories and empirical results. The authors utilize Bayesian Model Averaging (BMA) to resolve the model uncertainty that surrounds the validity of the competing FDI theories. Since the structure of existing FDI data is known to induce selection bias, they extend BMA theory to HeckitBMA to address model uncertainty in the presence of selection bias. They then show that more than half of the previously suggested FDI determinants are no longer robust and highlight theories that receive support from the data.