Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model

TL;DR

This paper introduces a time-varying parameter GVAR model incorporating machine learning for improved prediction and analysis of economic flows across developed regions, offering high accuracy and new insights into economic interconnectedness.

Contribution

It develops a novel TVP-GVAR framework with LASSO-based model selection, enabling effective out-of-sample prediction and analysis of economic variable connectedness in a machine learning context.

Findings

High in-sample fit for economic variables

Accurate out-of-sample predictions across frequencies

Novel insights into economic connectedness at key time points

Abstract

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.