A Gaussian smooth transition vector autoregressive model: An application to the macroeconomic effects of severe weather shocks
提出一种新的高斯平滑转换向量自回归模型，其转换权重由前p期观测的加权似然决定，能更好捕捉渐进体制转换。应用于美国月度数据（1961:1-2022:3），发现严重天气冲击在样本早期和某些危机时期的影响强于后期，可能支持美国经济对恶劣天气的长期适应。
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. While the overall evidence is somewhat mixed, this may lend some support to overall adaptation of the U.S. economy to severe weather over time.