Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models

TL;DR

This paper analyzes the statistical properties of break-point estimators in nonstationary autoregressive and predictive regression models, focusing on their asymptotic behavior and the impact of covariate persistence and break-point location.

Contribution

It provides new insights into the asymptotic distribution of break-point estimators considering nonstationarity and covariate persistence in autoregressive and predictive models.

Findings

Derived the limiting distribution of break-point estimators.

Showed how covariate persistence affects estimator behavior.

Analyzed the impact of break-point location on estimation accuracy.

Abstract

In this article, we study the statistical and asymptotic properties of break-point estimators in nonstationary autoregressive and predictive regression models for testing the presence of a single structural break at an unknown location in the full sample. Moreover, we investigate aspects such as how the persistence properties of covariates and the location of the break-point affects the limiting distribution of the proposed break-point estimators.