  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.
