  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 impacts of
the shock are stronger 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 increased
severe weather over time.
