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Tips and tricks for Bayesian VAR models in gretl

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  • Luca Pedini

    (Università Politecnica delle Marche)

Abstract

Bayesian Vector Autoregressive models have become the natural response to the dense parametrization often required by multivariate time series modeling. However, the Bayesian approach is somehow new to the gretl ecosystem: classical analysis of Vector Autoregressions (VARs) is natively supported and supplemented via addons and function packages, but a Bayesian counterpart in the form of equally general and advanced functions or, at a lower level, in the form of didactic example scripts is missing. This paper pursues the second route describing, via a replication exercise, how to perform basic Bayesian inference in gretl using VARs, with particular reference to structural analysis. The contribution goes in the direction of providing new hints and tools, available to the gretl user, for a more complete and up-to-date understanding of modern macroeconometrics.

Suggested Citation

  • Luca Pedini, 2024. "Tips and tricks for Bayesian VAR models in gretl," Computational Statistics, Springer, vol. 39(7), pages 3579-3597, December.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:7:d:10.1007_s00180-024-01492-3
    DOI: 10.1007/s00180-024-01492-3
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    Cited by:

    1. Marcin Błażejowski & Paweł Kufel & Jacek Kwiatkowski, 2026. "Bayesian model averaging for VAR models: gretl-based implementation," Computational Statistics, Springer, vol. 41(2), pages 1-19, February.
    2. Chiara Casoli & Luca Pedini, 2026. "Measuring spillovers and connectedness in gretl," Computational Statistics, Springer, vol. 41(1), pages 1-30, January.
    3. Paolo Chirico, 2026. "QMSLV: a gretl package for quasi maximum likelihood estimation of stochastic volatility models," Computational Statistics, Springer, vol. 41(1), pages 1-18, January.

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