Robust FDI Determinants: Bayesian Model Averaging in the Presence of Selection Bias

30 Pages Posted: 8 May 2012

See all articles by Theo S. Eicher

Theo S. Eicher

University of Washington - Department of Economics

Lindy Helfman

affiliation not provided to SSRN

Alex Lenkoski

Norwegian Computing Center

Date Written: November 7, 2011

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.

Suggested Citation

Eicher, Theo S. and Helfman, Lindy and Lenkoski, Alex, Robust FDI Determinants: Bayesian Model Averaging in the Presence of Selection Bias (November 7, 2011). Available at SSRN: https://ssrn.com/abstract=2054934 or http://dx.doi.org/10.2139/ssrn.2054934

Theo S. Eicher (Contact Author)

University of Washington - Department of Economics ( email )

Box 353330
Seattle, WA 98195-3330
United States

Lindy Helfman

affiliation not provided to SSRN

Alex Lenkoski

Norwegian Computing Center ( email )

P. O. Box 114 Blindern
0314 Oslo
Norway

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