Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates

TL;DR

This paper develops new statistical tests for detecting structural breaks in nonstationary quantile predictive regressions, ensuring robustness regardless of covariate persistence and demonstrating practical utility through simulations and real data analysis.

Contribution

It introduces IVX-based tests for structural break detection in nonstationary quantile regressions that are nuisance parameter-free and consistent under local alternatives.

Findings

IVX-based tests are asymptotically nuisance parameter-free.

The tests perform well in finite samples according to simulations.

Application to house price data shows practical relevance.

Abstract

We propose an econometric environment for structural break detection in nonstationary quantile predictive regressions. We establish the limit distributions for a class of Wald and fluctuation type statistics based on both the ordinary least squares estimator and the endogenous instrumental regression estimator proposed by Phillips and Magdalinos (2009a, Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University). Although the asymptotic distribution of these test statistics appears to depend on the chosen estimator, the IVX based tests are shown to be asymptotically nuisance parameter-free regardless of the degree of persistence and consistent under local alternatives. The finite-sample performance of both tests is evaluated via simulation experiments. An empirical application to house pricing index returns demonstrates the practicality of the proposed break tests for regression quantiles of nonstationary time series data.