Semiparametric forecasting problem in high dimensional dynamic panel with correlated random effects: a hierarchical empirical Bayes approach

A novel for multivariate dynamic panel data analysis with correlated random effects is proposed when estimating high dimensional parameter spaces. A semiparametric hierarchical Bayesian strategy is used to jointly deal with incidental parameters, endogeneity issues, and model misspecification problems. The underlying methodology involves addressing an \texttt{ad-hoc} model selection based on conjugate informative proper mixture priors to select promising subsets of predictors affecting outcomes. Monte Carlo algorithms are then conducted on the resulting submodels to construct empirical Bayes estimators and investigate ratio-optimality and posterior consistency for forecasting purposes and policy issues. An empirical approach to a large panel of economies is conducted describing the functioning of the model. Simulations based on Monte Carlo designs are also performed to account for relative regrets dealing with cross-sectional heterogeneity.