The Dynamic Persistence of Economic Shocks
提出一种新框架来建模经济时间序列中随时间变化的冲击持续性，应用于美国通胀和股市波动数据，发现持续性变化与宏观事件吻合，并提升了预测准确性。
Abstract We propose a novel framework for modeling time-varying persistence in economic time series, allowing for smoothly evolving heterogeneity in shock dynamics. We leverage localized regression techniques to flexibly identify changes in persistence over time, offering a data-driven alternative to traditional parametric models. We applied this methodology to U.S. inflation and stock market volatility data and found substantial persistence variations that align with key macroeconomic events and market conditions. The results reveal previously undetected pockets of predictability and provide significant increases in out-of-sample forecast accuracy. These findings have important implications for economic modeling, forecasting, and policy analysis.