Document Type
Discussion Paper
Publication Date
9-23-2024
CFDP Number
2389R1
CFDP Pages
40
Journal of Economic Literature (JEL) Code(s)
C1
Abstract
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 τ in time when the parameter exhibits unit root, local-to-unity, or stationary/stationary-like behavior at time τ. 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.
Recommended Citation
Andrews, Donald W.K. and Li, Ming, "Inference in a Stationary/Nonstationary Autoregressive Time-Varying-Parameter Model" (2024). Cowles Foundation Discussion Papers. 2818.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2818
