Natural disasters as macroeconomic tail risks
用分位数脉冲响应方法研究气候相关自然灾害如何影响产出增长和通胀的预测分布，发现灾害主要改变分布的尾部，并评估了更频繁大灾害对宏观经济的假设影响。
We introduce quantile and moment impulse response functions for structural quantile vector autoregressive models. We use them to study how climate-related natural disasters affect the predictive distribution of output growth and inflation. Disasters strongly shift the forecast distribution particularly in the tails. They result in an initial sharp increase of the downside risk for growth, followed by a temporary rebound. Upside risk to inflation increases markedly for a few months and then subsides. As a result, natural disasters have a persistent impact on the conditional variance and skewness of macroeconomic aggregates which standard linear models estimating conditional mean dynamics fail to match. We perform a scenario analysis to evaluate the hypothetical effects of more frequent large disasters on the macroeconomy due to increased atmospheric carbon concentration. Our results indicate a substantially higher conditional volatility of growth and inflation as well as increased upside risk to inflation particularly in a scenario where only currently pledged climate policies are implemented.