Tips and tricks for Bayesian VAR models in gretl

Luca Pedini

Computational Statistics, 2024, vol. 39, issue 7, No 6, 3579-3597

Abstract: 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.

Keywords: gretl; Bayesian inference; VAR model; Structural analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-024-01492-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:39:y:2024:i:7:d:10.1007_s00180-024-01492-3

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-024-01492-3

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().