One size does not fit all… panel data: Bayesian model averaging and data poolability

We show in this paper why researchers ought to pay particular attention to the issues of model uncertainty and data poolability in their panel data applications. We focus on the identification of robust determinants of current account balances (CABs). Applying Bayesian Model Averaging, we adopt a flexible modelling approach to highlight that (i) some determinants have limited relevance when accounting for model uncertainty; (ii) slope homogeneity is unlikely to be a valid assumption; iii) cross-sectional and time-series relationships can diverge. We explain why estimating cross-sectional estimates is valuable, even in the potential presence of an omitted variable bias, and suggest a way for assessing the effects of unobserved country heterogeneity.