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What are the policies and country-level conditions which best explain bilateral trade flows between countries? As databases expand, an increasing number of possible explanatory variables are proposed that influence bilateral trade without a clear indication of which variables are robustly important across contexts, time periods, and which are not sensitive to inclusion of other control variables. To shed light on this problem, we apply three model selection methods – Lasso reguarlized regression, Bayesian Model Averaging, and Extreme Bound Analysis -- to candidate variables in a gravity models of trade. Using a panel of 198 countries covering the years 1970 to 2000, we find model selection methods suggest many fewer variables are robust that those suggested by the null hypothesis rejection methodology from ordinary least squares.


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