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Bayesian regression models in gretl: the BayTool package

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  • Luca Pedini

    (Università Politecnica delle Marche)

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.

Suggested Citation

  • Luca Pedini, 2024. "Bayesian regression models in gretl: the BayTool package," Computational Statistics, Springer, vol. 39(7), pages 3547-3578, December.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:7:d:10.1007_s00180-024-01466-5
    DOI: 10.1007/s00180-024-01466-5
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    1. Fair, Ray C, 1978. "A Theory of Extramarital Affairs," Journal of Political Economy, University of Chicago Press, vol. 86(1), pages 45-61, February.
    2. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    3. Błażejowski, Marcin & Kwiatkowski, Jacek, 2015. "Bayesian Model Averaging and Jointness Measures for gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i05).
    4. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    5. Anglin, Paul M & Gencay, Ramazan, 1996. "Semiparametric Estimation of a Hedonic Price Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 633-648, Nov.-Dec..
    6. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, December.
    7. Dootika Vats & James M Flegal & Galin L Jones, 2019. "Multivariate output analysis for Markov chain Monte Carlo," Biometrika, Biometrika Trust, vol. 106(2), pages 321-337.
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