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Granger Causality Testing with Intensive Longitudinal Data

Granger Causality Testing with Intensive Longitudinal Data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Prevention Science Springer Journals

Granger Causality Testing with Intensive Longitudinal Data

Molenaar, Peter
Prevention Science , Volume 20 (3) – Jun 1, 2018
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10 pages

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Society for Prevention Research
Subject
Medicine & Public Health; Public Health; Health Psychology; Child and School Psychology
ISSN
1389-4986
eISSN
1573-6695
DOI
10.1007/s11121-018-0919-0
Publisher site
See Article on Publisher Site

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.

Journal

Prevention Science – Springer Journals

Published: Jun 1, 2018

References

  • A network theory of mental disorders
    Borsboom, D
  • Time series: Data analysis and theory
    Brillinger, DR
  • Extended causal modeling to assess partial directed coherence in multiple time series with significant instantaneous interactions
    Faes, L; Nollo, G
  • Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
    Gates, KM; Molenaar, PCM
  • Measurement of linear dependence and feedback between multiple time series
    Geweke, J
  • Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping
    Goebel, R; Roebroeck, A; Kim, DS; Formisano, E
  • Investigating causal relations by econometric models and cross-spectral methods
    Granger, CWJ
  • Testing for Granger causality in the frequency domain: A phase resampling method
    Liu, S; Molenaar, PCM
  • A dynamic factor model for the analysis of multivariate time series
    Molenaar, PCM
  • A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever
    Molenaar, PCM
  • The new person-specific paradigm in psychology
    Molenaar, PCM; Campbell, CG
  • Statistics and causality: Methods for applied empirical research
    Molenaar, PCM; Lo, LL
  • Causality: Models, reasoning and inference
    Pearl, J
  • Event-related dynamics of brain oscillations. Progress in brain research 159
    Schlögl, A; Supp, G
  • ITAREPS: Information technology aided relapse prevention programme in schizophrenia
    Spaniel, F; Vohlidka, P; Hrdlicka, J; Kozeny, J; Novak, T; Motlova, L; Cermak, J; Bednarik, J; Novak, D; Höschl, C
  • Ambulatory assessment
    Trull, TJ; Ebner-Priemer, U
  • Multivariate Granger causality: An estimation framework based on factorization of the spectral density matrix
    Wen, X; Rangarajan, G; Ding, M
  • Causality in time series. Challenges in machine learning 5
    White, H; Chalak, K; Lu, X