Penalized Bayesian Approach-Based Variable Selection for Economic Forecasting

Abstract

This paper proposes a penalized Bayesian computational algorithm as an improvement to the LASSO approach for economic forecasting in multivariate time series. Methodologically, a weighted variable selection procedure is involved in handling high-dimensional and highly correlated data, reduce the dimensionality of the model and parameter space, and then select a promising subset of predictors affecting the outcomes. It is weighted because of two auxiliary penalty terms involved in prior specifications and posterior distributions. The empirical example addresses the issue of pandemic disease prediction and the effects on economic development. It builds on a large set of European and non-European regions to also investigate cross-unit heterogeneity and interdependency. According to the estimation results, density forecasts are conducted to highlight how the promising subset of covariates would help to predict potential contagion due to pandemic diseases. Policy issues are also discussed.