Scalable Bayesian Estimation in the Multinomial Probit Model

Ruben Loaiza-Maya () and Didier Nibbering
Authors registered in the RePEc Author Service: Rubén Albeiro Loaiza Maya

No 25/20, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This paper proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating this structure is that the model parameters require identifying restrictions. We identify the parameters by a trace-restriction on the covariance matrix, which is imposed through a reparamatrization of the factor structure. We specify interpretable prior distributions on the model parameters and develop an MCMC sampler for parameter estimation. The proposed approach substantially improves performance in large choice sets relative to existing multinomial probit specifications. Applications to purchase data show the economic importance of including a large number of choice alternatives in consumer choice analysis.

Keywords: multinomial probit model; factor analysis; parameter identification; spherical coordinates (search for similar items in EconPapers)
JEL-codes: C11 C25 C35 C38 (search for similar items in EconPapers)
Pages: 36
Date: 2020
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-ore and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.monash.edu/business/ebs/research/publications/ebs/wp25-2020.pdf (application/pdf)

Related works:
Working Paper: Scalable Bayesian estimation in the multinomial probit model (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2020-25

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().