Introduction to Bayesian model averaging in Stata

Gustavo Sánchez
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Gustavo Sánchez: StataCorp

Mexican Stata Conference 2023 from Stata Users Group

Abstract: Model selection represents a key aspect in regression analysis. Most empirical applications consider a fixed unknown underlying data-generating model (DGM) that researchers try to find, based on a particular theoretical framework that is combined with the data associated with the variables involved in the selected model specification. Bayesian model averaging provides an approach, where instead of focusing the estimation on the search for that unique unknown model, researchers can incorporate the uncertainty about the DMG to obtain probabilities associated with relevant predictors, measurements about complementary or substitutable predictors across different model candidates, and also predictions that incorporate uncertainty about the model and the parameters. In this presentation, I will use the new suite of bma commands to illustrate those and other aspects that can be derived using Bayesian model averaging.

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http://repec.org/mex2023/Mexico23_Sanchez.pdf presentation materials (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:boc:mexi23:04

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