Semiparametric Forecasting Problem in High Dimensional Dynamic Panel with Correlated Random Effects: A Hierarchical Empirical Bayes Approach

This paper aims to address semiparametric forecasting problem when studying high dimensional data in multivariate dynamic panel model with correlated random effects. A hierarchical empirical Bayesian perspective is developed to jointly deal with incidental parameters, structural framework, unobserved heterogeneity, and model misspecification problems. Methodologically, an ad-hoc model selection on a mixture of normal distributions is addressed to obtain the best combination of outcomes to construct empirical Bayes estimator and then investigate ratio-optimality and posterior consistency for better individual–specific forecasts. Simulations for Monte Carlo designs are performed to account for relative regrets dealing with correlated random effects distribution. A real case-study on the current COVID-19 pandemic crisis among a pool of developed and emerging economies is also conducted to highlight the performance of the estimating procedure.