Inference in a Stationary/Nonstationary Autoregressive Time-Varying-Parameter Model

TL;DR

This paper develops a nonparametric method for estimating and conducting inference on time-varying AR(1) models that can smoothly transition between stationary and nonstationary states, providing robust confidence intervals.

Contribution

It introduces a local least squares estimation approach for AR parameters in models with deterministic time-varying stationarity and nonstationarity, including unit root behavior.

Findings

Derived limit distributions for AR parameter estimators and t-statistics.

Constructed confidence intervals with correct asymptotic coverage.

Validated the approach for models with smooth transitions between stationarity and nonstationarity.

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 $\tau$ 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.