🤖 AI Summary
This paper addresses the modeling challenge of smoothly evolving shock persistence in economic time series, departing from conventional assumptions of homogeneity or piecewise-constant persistence. Methodologically, it introduces the concept of “dynamic local persistence” and develops a time-varying coefficient framework, integrating local regression with rolling-window estimation to nonparametrically and granularly identify the decay dynamics of shocks. Empirical applications to inflation and stock market volatility reveal pronounced, continuously evolving persistence structures: inflation shock persistence exhibits a systematic decline after the mid-2000s, whereas equity volatility shock persistence markedly increases around the global financial crisis. The proposed framework delivers an interpretable and estimable tool for analyzing heterogeneous macroeconomic policy transmission and dynamic asset risk pricing.
📝 Abstract
This paper presents a model for smoothly varying heterogeneous persistence of economic data. We argue that such dynamics arise naturally from the dynamic nature of economic shocks with various degree of persistence. The identification of such dynamics from data is done using localised regressions. Empirically, we identify rich persistence structures that change smoothly over time in two important data sets: inflation, which plays a key role in policy formulation, and stock volatility, which is crucial for risk and market analysis.