Granger Causality Analysis

Granger Causality Analysis is a statistical method for inferring directed functional interactions between time series by determining whether past values of one signal provide unique information that improves the prediction of another signal beyond what is achievable using only its own past values.

5.6K

Publications

293.6K

Citations

11.3K

Authors

3.5K

Institutions

Publications per year

2017–2026

2.3K

Variants

Granger Causality

Authors

11.3K

  1. MD
    Mingzhou Ding

    University of Florida, United States

    36 publications · 7.7K citations

    31

    h-index

  2. MS
    Muhammad Shahbaz

    COMSATS University Islamabad, Pakistan

    48 publications · 4.9K citations

    30

    h-index

  3. DM
    Daniele Marinazzo

    Ghent University, Belgium

    22 publications · 2.2K citations

    22

    h-index

  4. TC
    Tsangyao Chang

    Feng Chia University, Taiwan

    32 publications · 1.9K citations

    22

    h-index

  5. RG
    Rangan Gupta

    University of Pretoria, South Africa

    31 publications · 1.4K citations

    21

    h-index

1–5 of 11.3K

Venues

  1. Applied Economics

    127 publications · 8.8K citations

    53

    h-index

  2. SSRN Electronic Journal

    Repository · 196 publications · 5.8K citations

    40

    h-index

  3. PLoS ONE

    79 publications · 4.4K citations

    39

    h-index

  4. RePEc: Research Papers in Economics

    Repository · 160 publications · 7.2K citations

    39

    h-index

  5. Sustainability

    94 publications · 4.2K citations

    38

    h-index

1–5