Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2025

Abstract

Time series models are often fitted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fitting models with spurious dynamics that lead to unsupported and controversial conclusions about time dependence, causality, and the effects of unanticipated shocks. In spite of what may seem as obvious differences between a time series of independent variates with changing variance and a stationary conditionally heteroskedastic (GARCH) process, such processes may be hard to distinguish in applied work using basic time series diagnostic tools. We develop and study some practical and easily implemented statistical procedures to test the mean and variance stability of uncorrelated and serially dependent time series. Application of the new methods to analyze the volatility properties of stock market returns leads to some unexpectedly surprising findings concerning the advantages of modeling time-varying changes in unconditional variance.

Keywords

heteroskedasticity, KPSS test, mean stability, variance stability, VS test

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Time Series Analysis

First Page

1

Last Page

19

ISSN

0143-9782

Identifier

10.1111/jtsa.12840

Publisher

Wiley

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1111/jtsa.12840

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