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Granger Causality Testing with Intensive Longitudinal Data.

Peter C M Molenaar1

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Summary
This summary is machine-generated.

Intensive longitudinal data enable subject-specific dynamic networks via vector autoregressive (VAR) modeling. A new hybrid VAR approach optimizes Granger causality testing for identifying causal relations in prevention research.

Keywords:
Granger causalityHybrid VARPartial directed coherenceStandard VARStructural VAR

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Area of Science:

  • Psychology
  • Network Science
  • Statistics

Background:

  • Intensive longitudinal data from ambulatory assessment offer new avenues for prevention research.
  • Vector autoregressive (VAR) modeling can derive subject-specific dynamic networks from such data.
  • Identifying causal relations within these networks is crucial for effective interventions.

Purpose of the Study:

  • To introduce a novel hybrid vector autoregressive (VAR) representation for dynamic networks.
  • To provide data-driven criteria for selecting the optimal VAR representation.
  • To enhance Granger causality testing for improved causal inference in prevention research.

Main Methods:

  • Development of hybrid VAR models as an alternative representation to standard and structural VARs.
  • Application of Granger causality testing to identify causal relationships in time-dependent variables.
  • Utilizing partial directed coherence in the frequency domain for robust causality assessment.

Main Results:

  • The proposed hybrid VARs offer a data-driven method for selecting the optimal representation.
  • Granger causality testing performs optimally when utilizing the hybrid VAR representation.
  • Partial directed coherence demonstrates superior performance with hybrid VAR-based analyses.

Conclusions:

  • Hybrid VARs provide a crucial advancement for analyzing complex dynamic networks in prevention research.
  • This approach facilitates more reliable identification of causal pathways using Granger causality.
  • The findings support the use of hybrid VARs for data-driven causal inference from intensive longitudinal data.