We propose a Bayesian approach to dynamic panel estimation in the presence of cross-sectional dependence and dynamic heterogeneity which is suitable for inference in short panels, unlike alternative estimators. Monte Carlo simulations indicate that our estimator produces less bias, and a lower root mean squared error, than existing estimators. The method is illustrated by estimating a panel VAR on sector level data for labour productivity and hours worked growth for Canada, Germany, France, Italy, the UK and the US from 1992 Q1 to 2011 Q3. We use historical decompositions to examine the determinants of recent output growth in each country. This exercise demonstrates that failure to take cross-sectional dependence into account leads to highly misleading results.
Bayesian dynamic panel estimator dynamic heterogeneity cross-sectional dependence labour productivity.