dc.contributor.author | Giraitis, L | en_US |
dc.contributor.author | Li, Y | en_US |
dc.contributor.author | Phillips, PCB | en_US |
dc.date.accessioned | 2024-02-13T13:54:18Z | |
dc.date.available | 2024-01-19 | en_US |
dc.identifier.citation | Liudas Giraitis, Yufei Li, Peter C.B. Phillips,
Robust inference on correlation under general heterogeneity,
Journal of Econometrics,
Volume 240, Issue 1,
2024,
105691,
ISSN 0304-4076,
https://doi.org/10.1016/j.jeconom.2024.105691.
(https://www.sciencedirect.com/science/article/pii/S030440762400037X)
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: Serial correlation; Cross-correlation; Heteroskedasticity; Martingale differences | |
dc.identifier.issn | 1872-6895 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94600 | |
dc.description.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. | |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Journal of Econometrics | en_US |
dc.rights | © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Serial correlation | en_US |
dc.subject | cross-correlation | en_US |
dc.subject | heteroskedasticity | en_US |
dc.subject | martingale differences | en_US |
dc.title | Robust Inference on Correlation under General Heterogeneity | en_US |
dc.type | Article | |
dc.identifier.doi | doi.org/10.1016/j.jeconom.2024.105691 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2024-01-19 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |