Measuring Conditional Persistence in Nonlinear Time Series*
提出一种基于非参数估计的条件期望导数的方法，用于度量经济时间序列中冲击的持续性如何随历史状态变化，并通过蒙特卡洛模拟和实际汇率数据验证其有效性。
Abstract The persistence properties of economic time series have been a primary object of investigation in a variety of guises since the early days of econometrics. Recently, work on nonlinear modelling for time series has introduced the idea that persistence of a shock at a point in time may vary depending on the state of the process at that point in time. This article suggests investigating the persistence of processes conditioning on their history as a tool that may aid parametric nonlinear modelling. In particular, we suggest that examining the nonparametrically estimated derivatives of the conditional expectation of a variable with respect to its lag(s) may be a useful indicator of the variation in persistence with respect to its past history. We discuss in detail the implementation of the measure and present a Monte Carlo investigation. We further apply the persistence analysis to real exchange rates.