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).
Measuring spillovers and connectedness in gretl / Casoli, C.; Pedini, L.. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 41:1(2026). [10.1007/s00180-025-01680-9]
Measuring spillovers and connectedness in gretl
Pedini L.
2026-01-01
Abstract
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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
