eprintid: 10071424
rev_number: 21
eprint_status: archive
userid: 608
dir: disk0/10/07/14/24
datestamp: 2019-04-02 10:37:30
lastmod: 2020-08-07 16:30:58
status_changed: 2019-09-24 15:37:51
type: article
metadata_visibility: show
creators_name: Smith, A
creators_name: Allenby, G
title: Demand Models with Random Partitions
ispublished: pub
divisions: UCL
divisions: A01
divisions: B04
divisions: C05
divisions: F49
keywords: Bayesian inference, location-scale family, Polya urn, Markov chain Monte Carlo, price elasticity
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in applied problems when the product space is large or spans multiple categories. While the partition is traditionally fixed a priori, we let the partition be a model parameter and propose a Bayesian method for inference. The challenge is that demand systems are commonly multivariate models that are not conditionally conjugate with respect to partition indices, precluding the use of Gibbs sampling. We solve this problem by constructing a new location-scale partition distribution that can generate random-walk Metropolis-Hastings proposals and
also serve as a prior. Our method is illustrated in the context of a store-level category demand model where we find that allowing for partition uncertainty is important for preserving model flexibility, improving demand forecasts, and learning about the structure of demand.
date: 2020
date_type: published
publisher: American Statistical Association
official_url: https://doi.org/10.1080/01621459.2019.1604360
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1644194
doi: 10.1080/01621459.2019.1604360
lyricists_name: Smith, Adam
lyricists_id: ASMIA85
actors_name: Smith, Adam
actors_id: ASMIA85
actors_role: owner
full_text_status: public
publication: Journal of the American Statistical Association
volume: 115
number: 529
pagerange: 47-65
issn: 0162-1459
citation:        Smith, A;    Allenby, G;      (2020)    Demand Models with Random Partitions.                   Journal of the American Statistical Association , 115  (529)   pp. 47-65.    10.1080/01621459.2019.1604360 <https://doi.org/10.1080/01621459.2019.1604360>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10071424/1/dmrp.pdf