论文标题
解释和预测经济流动：随时间变化的参数全球矢量自回归整合了机器学习模型
Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model
论文作者
论文摘要
本文提出了一个随时间变化的参数全球矢量自回归（TVP-GVAR）框架，用于预测和分析开发的地区经济变量。我们希望为经济应用程序设置提供一种易于访问的方法，其中可以将各种机器学习模型纳入样本外预测。选择了用于数值有效模型的平均平方误差（MSE）的套索型技术。我们在所有经济变量中表明了我们提出的模型的令人信服的样本表现，并通过不同的频率经济投入进行了相对较高的样本外预测。此外，随着时间变化的正交冲动反应为跨发达地区的关键时间点上经济变量的联系提供了新的见解。我们还在标准假设下得出了正交脉冲响应功能的相应渐近条带（置信区间）。
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 in-sample performance of our proposed model in all economic variables and relatively high precision out-of-sample predictions with different-frequency economic inputs. Furthermore, the time-varying orthogonal impulse responses provide novel insights into the connectedness of economic variables at critical time points across developed regions. We also derive the corresponding asymptotic bands (the confidence intervals) for orthogonal impulse responses function under standard assumptions.
