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Published in:

01-04-2019

Granger Causality Testing with Intensive Longitudinal Data

Author: Peter C. M. Molenaar

Published in: Prevention Science | Issue 3/2019

Abstract

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.

I thank an anonymous reviewer for pointing out the distinctions between networks.

Thanks are due to Dr. Matthew Goodwin, Northeastern University, for allowing to use this data.

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Title: Granger Causality Testing with Intensive Longitudinal Data
Author: Peter C. M. Molenaar
Publication date: 01-04-2019
Publisher: Springer US
Published in: Prevention Science / Issue 3/2019
Print ISSN: 1389-4986
Electronic ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-018-0919-0

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Granger Causality Testing with Intensive Longitudinal Data

Abstract

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Abstract

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