Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model
融合机器学习的时变参数全球向量自回归：经济动态解释与预测
The paper proposes a time-varying parameter global vector autoregressive (TVP-GVAR) framework for predicting and analysing developed region economic variables. We want to provide an easily accessible approach for the economy application settings, where a variety of machine learning models can be incorporated for out-of-sample prediction. The LASSO-type technique for numerically efficient model selection of mean squared errors (MSEs) is selected. We show the convincing…