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.

中文翻译：

---

基于惩罚贝叶斯方法的经济预测变量选择

本文提出了一种惩罚贝叶斯计算算法，作为多元时间序列经济预测 LASSO 方法的改进。从方法上讲，加权变量选择过程涉及处理高维和高度相关的数据，降低模型和参数空间的维数，然后选择影响结果的有希望的预测变量子集。它被加权是因为先验规范和后验分布中涉及两个辅助惩罚项。这个实证例子解决了流行病预测及其对经济发展的影响的问题。它建立在大量欧洲和非欧洲地区的基础上，还调查跨单位的异质性和相互依赖性。根据估计结果，进行密度预测是为了强调有希望的协变量子集如何帮助预测流行病引起的潜在传染。政策问题也被讨论。