Granger Causality Testing with Intensive Longitudinal Data

Peter C.M. Molenaar

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Original languageEnglish (US)
Pages (from-to)442-451
Number of pages10
JournalPrevention Science
Issue number3
StatePublished - Apr 15 2019

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health


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