Prevention Science

, Volume 20, Issue 3, pp 442–451 | Cite as

Granger Causality Testing with Intensive Longitudinal Data

  • Peter C. M. MolenaarEmail author


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.


Granger causality Standard VAR Structural VAR Hybrid VAR Partial directed coherence 



Funding of the research presented in this paper was partially provided by NSF 1157220 (PI PCM Molenaar).

Compliance with Ethical Standards

Conflict of Interest

The author declares that there is no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by the author.

Informed Consent

Informed consent was not required for this study.


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Copyright information

© Society for Prevention Research 2018

Authors and Affiliations

  1. 1.The Pennsylvania State UniversityUniversity ParkUSA

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