Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2024

Abstract

Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or zero cross-correlation when time series are not independent identically distributed random variables, pointing to the need for more robust procedures. Recent tests for serial correlation and cross-correlation in Dalla, Giraitis, and Phillips (2022) provide a more robust approach, allowing for heteroskedasticity and dependence in uncorrelated data under restrictions that require a smooth, slowly-evolving deterministic heteroskedasticity process. The present work removes those restrictions and validates the robust testing methodology for a wider class of innovations and regression residuals allowing for heteroscedastic uncorrelated and non-stationary data settings. The updated analysis given here enables more extensive use of the methodology in practical applications. Monte Carlo experiments confirm excellent finite sample performance of the robust test procedures even for extremely complex white noise processes. The empirical examples show that use of robust testing methods can materially reduce spurious evidence of correlations found by standard testing procedures.

Keywords

Cross-correlation, Heteroskedasticity, Martingale differences, Serial correlation

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

240

Issue

1

First Page

1

Last Page

19

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2024.105691

Publisher

Elsevier: 24 months

Copyright Owner and License

Authors-CC-BY-NC

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/10.1016/j.jeconom.2024.105691

Download

Included in

Econometrics Commons

Share

COinS