Robust residual cross correlation tests for lagged relations in time series

Wai Keung LI, Y. V HUI

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Testing for lagged relations between two time series is an important problem with many applications. Some examples include transfer function modelling and the well known Granger causality problem in econometrics. The usual tests based on residual cross correlations are not robust to even one or two outliers within the series. In this paper a robustified residual cross correlation is defined. The asymptotic distribution of the residual cross correlations based on this definition is obtained under very general conditions. From this result several robustified residual cross correlation tests for lagged relations are derived. These tests appear to be more resistant to outliers in terms of both size and power in simulation experiments. Copyright © 1994 Taylor & Francis Group, LLC. All rights reserved.
Original languageEnglish
Pages (from-to)103-109
JournalJournal of Statistical Computation and Simulation
Volume49
Issue number1-2
DOIs
Publication statusPublished - 1994

Fingerprint

Cross-correlation
Transfer functions
Time series
Testing
Outlier
Experiments
Granger Causality
Econometrics
Transfer Function
Asymptotic distribution
Simulation Experiment
Series
Modeling
Outliers

Citation

Li, W. K., & Hui, Y. V. (1994). Robust residual cross correlation tests for lagged relations in time series. Journal of Statistical Computation and Simulation, 49(1-2), 103-109. doi: 10.1080/00949659408811563

Keywords

  • Asymptotic distribution
  • Autoregressive moving average models
  • Causality tests
  • Residual autocovariance estimates
  • Residual cross correlations