Inference in a stationary/nonstationary autoregressive time-varying-parameter model

• 成果类型: Article
• 署名作者: Andrews, Donald W. K.; Li, Ming
• 署名单位: Yale University; National University of Singapore; National University of Singapore
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2465
• 发表日期: 2025
• 页码: 823-858
• 关键词: Autoregressive time-varying-parameter model; endogenous initial condition; Nonparametric Estimation; Confidence Interval; C10; C14

摘要

This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters. A key feature of the proposed approach is to allow for time-varying stationarity in some time periods, time-varying nonstationarity (i.e., unit root or local-to-unit root behavior) in other periods, and smooth transitions between the two. The estimation of the AR parameter at any time point is based on a local least squares regression method, where the relevant initial condition is endogenous. We obtain limit distributions for the AR parameter estimator and t-statistic at a given point tau in time when the parameter exhibits unit root, local-to-unity, or stationary/stationary-like behavior at time tau. These results are used to construct confidence intervals and median-unbiased interval estimators for the AR parameter at any specified point in time. The confidence intervals have correct asymptotic coverage probabilities with the coverage holding uniformly over stationary and nonstationary behavior of the observations.