A Gaussian smooth transition vector autoregressive model: An application to the macroeconomic effects of severe weather shocks

TL;DR

This paper proposes a new Gaussian smooth transition vector autoregressive model that captures gradual regime shifts in macroeconomic data, demonstrated through an analysis of the U.S. economy's response to severe weather shocks over several decades.

Contribution

It introduces a novel data-driven smooth transition VAR model with Gaussian distribution, improving the detection of complex, gradual regime changes in macroeconomic time series.

Findings

Severe weather shocks have a stronger impact in earlier and crisis regimes.

The U.S. economy shows adaptation to severe weather over time.

The model captures complex switching dynamics effectively.

Abstract

We introduce a new smooth transition vector autoregressive model with a Gaussian conditional distribution and transition weights that, for a $p$th order model, depend on the full distribution of the preceding $p$ observations. Specifically, the transition weight of each regime increases in its relative weighted likelihood. This data-driven approach facilitates capturing complex switching dynamics, enhancing the identification of gradual regime shifts. In an empirical application to the macroeconomic effects of a severe weather shock, we find that in monthly U.S. data from 1961:1 to 2022:3, the shock has stronger impact in the regime prevailing in the early part of the sample and in certain crisis periods than in the regime dominating the latter part of the sample. This suggests overall adaptation of the U.S. economy to severe weather over time.