This article presents the gretl package BayTool which integrates the software functionalities, mostly concerned with frequentist approaches, with Bayesian estimation methods of commonly used econometric models. Computational efficiency is achieved by pairing an extensive use of Gibbs sampling for posterior simulation with the possibility of splitting single-threaded experiments into multiple cores or machines by means of parallelization. From the user’s perspective, the package requires only basic knowledge of gretl scripting to fully access its functionality, while providing a point-and-click solution in the form of a graphical interface for a less experienced audience. These features, in particular, make BayTool stand out as an excellent teaching device without sacrificing more advanced or complex applications.

Bayesian regression models in gretl: the BayTool package / Pedini, L.. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 39:7(2024), pp. 3547-3578. [10.1007/s00180-024-01466-5]

Bayesian regression models in gretl: the BayTool package

Pedini L.
Primo
2024-01-01

Abstract

This article presents the gretl package BayTool which integrates the software functionalities, mostly concerned with frequentist approaches, with Bayesian estimation methods of commonly used econometric models. Computational efficiency is achieved by pairing an extensive use of Gibbs sampling for posterior simulation with the possibility of splitting single-threaded experiments into multiple cores or machines by means of parallelization. From the user’s perspective, the package requires only basic knowledge of gretl scripting to fully access its functionality, while providing a point-and-click solution in the form of a graphical interface for a less experienced audience. These features, in particular, make BayTool stand out as an excellent teaching device without sacrificing more advanced or complex applications.
2024
Bayesian methods; Gibbs sampling; Gretl; Parallelization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/355634
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