# Structural Change in (Economic) Time Series

## TL;DR

This paper reviews methods for detecting structural change points in univariate time series, compares recent techniques on oil prices and stock market data, highlighting their agreement and differences over time.

## Contribution

It provides a comparative analysis of recent methods for identifying structural changes in time series data, focusing on retrospective univariate approaches.

## Key findings

- Methods agree on early data up to mid-1980s
- Differences emerge in later data due to gradual trends
- Application to oil prices and stock index volatility

## Abstract

Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. Several recent methods for dating structural changes are compared using a time series of oil prices spanning more than 60 years. The methods broadly agree for the first part of the series up to the mid-1980s, for which changes are associated with major historical events, but provide somewhat different solutions thereafter, reflecting a gradual increase in oil prices that is not well described by a step function. As a further illustration, 1990s data on the volatility of the Hang Seng stock market index are reanalyzed.