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Bayesian model averaging

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  • Yulia Marchenko

    (StataCorp)

Abstract

Model uncertainty accompanies many data analyses. Stata's new bma suite that performs Bayesian model averaging (BMA) helps address this uncertainty in the context of linear regression. Which predictors are important given the observed data? Which models are more plausible? How do predictors relate to each other across different models? BMA can answer these and more questions. BMA uses the Bayes theorem to aggregate the results across multiple candidate models to account for model uncertainty during inference and prediction in a principled and universal way. In my presentation, I will describe the basics of BMA and demonstrate it with the bma suite. I will also show how BMA can become a useful tool for your regression analysis, Bayesian or not!

Suggested Citation

  • Yulia Marchenko, 2023. "Bayesian model averaging," 2023 Stata Conference 17, Stata Users Group.
    • Handle: RePEc:boc:usug23:17
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    File URL: http://repec.org/usug2023/US23_Marchenko.pdf
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    References listed on IDEAS

    as
    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
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