Quotation Grün, Bettina, Hofmarcher, Paul. 2021. Identifying Groups of Determinants in Bayesian Model Averaging Using Dirichlet Process Clustering. Scandinavian Journal of Statistics. 48 (3), 1018-1045.




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.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Scandinavian Journal of Statistics
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-B, WH-B
Language English
Title Identifying Groups of Determinants in Bayesian Model Averaging Using Dirichlet Process Clustering
Volume 48
Number 3
Year 2021
Page from 1018
Page to 1045
Reviewed? Y
URL https://onlinelibrary.wiley.com/doi/10.1111/sjos.12541
DOI https://doi.org/10.1111/sjos.12541
Open Access Y
Open Access Link https://doi.org/10.1111/sjos.12541


Grün, Bettina (Details)
Hofmarcher, Paul (Former researcher)
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1105 Computer software (Details)
1113 Mathematical statistics (Details)
5701 Applied statistics (Details)
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