Sequential Break-Point Detection in Stationary Time Series: An Application to Monitoring Economic Indicators

TL;DR

This paper evaluates the effectiveness of sequential break-point detection methods in stationary time series, specifically applied to economic indicators during financial crises, highlighting their dependence on break severity and timing.

Contribution

It provides a comprehensive simulation-based analysis of sequential break-point detectors for stationary time series in economic monitoring, offering practical insights for financial stability assessment.

Findings

Detection performance varies with break severity and location.

AR(1) and AR(1)-GARCH(1,1) models show different sensitivities.

Results inform practitioners on effective monitoring strategies.

Abstract

Monitoring economic conditions and financial stability with an early warning system serves as a prevention mechanism for unexpected economic events. In this paper, we investigate the statistical performance of sequential break-point detectors for stationary time series regression models with extensive simulation experiments. We employ an online sequential scheme for monitoring economic indicators from the European as well as the American financial markets that span the period during the 2008 financial crisis. Our results show that the performance of these tests applied to stationary time series regressions such as the AR(1) as well as the AR(1)-GARCH(1,1) depend on the severity of the break as well as the location of the break-point within the out-of-sample period. Consequently, our study provides some useful insights to practitioners for sequential break-point detection in economic and financial conditions.