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• W3121484917 abstract "Abstract The literature on Foreign Direct Investment (FDI) determinants is remarkably diverse in terms of competing theories and empirical results. We 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 well known to induce selection bias, we extend BMA theory to HeckitBMA in order to address model uncertainty in the presence of selection bias. We show that more than half of the previously suggested FDI determinants are not robust and highlight theories that do receive robust support from the data. Our selection approach allows us to identify the determinants of the margins of FDI (intensive and extensive), which are shown to differ profoundly. Our results suggest a new emphasis in FDI theories that explicitly identify the dynamics of the intensive and extensive FDI margins." @default.
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• W3121484917 date "2012-09-01" @default.
• W3121484917 modified "2023-10-16" @default.
• W3121484917 title "Robust FDI determinants: Bayesian Model Averaging in the presence of selection bias" @default.
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• W3121484917 doi "https://doi.org/10.1016/j.jmacro.2012.01.010" @default.
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