Copula-Based Univariate Time Series Structural Shift Identification Test

TL;DR

This paper introduces a copula-based method for detecting structural shifts in univariate time series by modeling non-linear dependencies among lagged values.

Contribution

It presents a novel approach using copulas to identify structural shifts assuming non-linear dependence, which improves detection accuracy.

Findings

Effective in modeling non-linear dependencies

Successfully detects structural shifts in simulated data

Offers a new tool for time series analysis

Abstract

An approach is proposed to determine structural shift in time-series assuming non-linear dependence of lagged values of dependent variable. Copulas are used to model non-linear dependence of time series components.