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<p>This article introduces a new <Emphasis FontCategory="SansSerif">gretl</Emphasis> package for computing connectedness measures, as proposed by&#xa0;Diebold and Yilmaz (2009) and extended by Diebold and Yilmaz (2012; 2014, hereafter DY). The <i>h</i>-step ahead connectedness indices, as defined by DY, are based on the variance decomposition, derived from the estimation of a Vector Autoregressive (VAR) model. We provide <Emphasis FontCategory="SansSerif">gretl</Emphasis> functions for computing static and dynamic connectedness indices. Additionally, we introduce a bootstrap-based technique for detecting statistically significant changes in connectedness, following Greenwood-Nimmo et&#xa0;al. (2024). Finally, we test our procedure by replicating the global stock market returns analysis of Diebold and Yilmaz (2009).</p>

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Measuring spillovers and connectedness in gretl

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Chiara Casoli,
  • Luca Pedini

摘要

This article introduces a new gretl package for computing connectedness measures, as proposed by Diebold and Yilmaz (2009) and extended by Diebold and Yilmaz (2012; 2014, hereafter DY). The h-step ahead connectedness indices, as defined by DY, are based on the variance decomposition, derived from the estimation of a Vector Autoregressive (VAR) model. We provide gretl functions for computing static and dynamic connectedness indices. Additionally, we introduce a bootstrap-based technique for detecting statistically significant changes in connectedness, following Greenwood-Nimmo et al. (2024). Finally, we test our procedure by replicating the global stock market returns analysis of Diebold and Yilmaz (2009).