Foreign direct investment (FDI) is marked with model uncertainty due to different theories and conclusions as to what variables should be included in the model. Using two specifications; the global sample and developing countries, I utilize the Bayesian Model Averaging (BMA) technique to determine the robust variables. Since the FDI modes; Greenfield Investment (GFI) and Mergers \& Acquisitions (M\&A) have different impacts on the economy, the BMA method is used to tackle separately model uncertainty in the two modes. My findings show that some of the variables identified by previous literature as determinants of FDI are not robust. In examining the FDI modes, I show that they behave in different ways having only six determinants in common. This study provides a better understanding of the robust determinants of FDI and the different ways GFI and M\&A responds to policies.