Document Type
Discussion Paper
Publication Date
12-15-2022
CFDP Number
2354
CFDP Pages
58
Journal of Economic Literature (JEL) Code(s)
C12
Abstract
Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or 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 un-correlated 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 heteroskedastic time series models and innovations. The updated analysis given here enables more extensive use of the method-ology 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.
Recommended Citation
Giraitis, Liudas; Li, Yugei; and Phillips, Peter C. B., "Robust Inference on Correlation under General Heterogeneity" (2022). Cowles Foundation Discussion Papers. 2734.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2734