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. We utilize Bayesian Model Averaging (BMA) to resolve the model uncertainty that surrounds the validity of the competing FDI theories. Since the structure of exis...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
22 February 2012
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| In: |
Journal of macroeconomics
Year: 2012, Volume: 34, Issue: 3, Pages: 637-651 |
| ISSN: | 0164-0704 |
| DOI: | 10.1016/j.jmacro.2012.01.010 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1016/j.jmacro.2012.01.010 Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S0164070412000274 
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| Author Notes: | Theo S. Eicher, Lindy Helfman, Alex Lenkoski |
| Summary: | 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. |
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| Item Description: | Gesehen am 25.06.2019 |
| Physical Description: | Online Resource |
| ISSN: | 0164-0704 |
| DOI: | 10.1016/j.jmacro.2012.01.010 |