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Bayesian model averaging

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  • Meghan Cain

    (StataCorp)

Abstract

Are you unsure which predictors to include in your model? Rather than choosing one model, aggregate results across all candidate models to account for model uncertainty with Bayesian model averaging (BMA). 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 questions and many more. Stata 18 introduced the bma suite of commands to perform BMA in linear regression models. In this talk, you will learn how to explore inQuential models, make inferences, and obtain better predictions with BMA. I will demonstrate the utility of BMA for any researcher—Bayesian, frequentist, and everyone in between! No prior knowledge of the Bayesian framework is required.

Suggested Citation

  • Meghan Cain, 2024. "Bayesian model averaging," Italian Stata Users' Group Meetings 2024 09, Stata Users Group.
    • Handle: RePEc:boc:isug24:09
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