Generalized Local‐to‐Unity Models
将流行的局部单位根模型推广到允许p个自回归根和p-1个移动平均根接近单位根，证明该模型能很好逼近一大类过程，并发现Campbell和Yogo的推断方法在GLTU(2)模型中失效，提出有限信息贝叶斯框架用于推断。
We introduce a generalization of the popular local‐to‐unity model of time series persistence by allowing for p autoregressive (AR) roots and p − 1 moving average (MA) roots close to unity. This generalized local‐to‐unity model, GLTU( p ), induces convergence of the suitably scaled time series to a continuous time Gaussian ARMA( p , p − 1) process on the unit interval. Our main theoretical result establishes the richness of this model class, in the sense that it can well approximate a large class of processes with stationary Gaussian limits that are not entirely distinct from the unit root benchmark. We show that Campbell and Yogo's (2006) popular inference method for predictive regressions fails to control size in the GLTU(2) model with empirically plausible parameter values, and we propose a limited‐information Bayesian framework for inference in the GLTU( p ) model and apply it to quantify the uncertainty about the half‐life of deviations from purchasing power parity.