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Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering
Author(s) -
Grün Bettina,
Hofmarcher Paul
Publication year - 2021
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12541
Subject(s) - prior probability , dirichlet process , mathematics , dirichlet distribution , markov chain monte carlo , bayesian probability , cluster analysis , econometrics , gibbs sampling , covariate , statistics , collinearity , mixture model , mathematical analysis , boundary value problem
Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) takes model uncertainty into account and identifies robust determinants. However, it requires the specification of suitable model priors. Mixture model priors are appealing because they explicitly account for different groups of covariates as robust determinants. Specific Dirichlet process clustering (DPC) model priors are proposed; their correspondence to the binomial model prior derived and methods to perform the BMA analysis including a DPC postprocessing procedure to identify groups of determinants are outlined. The application of these model priors is demonstrated in a simulation exercise and in an empirical analysis of cross‐country economic growth data. The BMA analysis is performed using the Markov chain Monte Carlo model composition sampler to obtain samples from the posterior of the model specifications. Results are compared with those obtained under a beta‐binomial and a collinearity‐adjusted dilution model prior.