TY  - JOUR
PB  - American Statistical Association
ID  - discovery10071424
N2  - 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.
KW  - Bayesian inference
KW  -  location-scale family
KW  -  Polya urn
KW  -  Markov chain Monte Carlo
KW  -  price elasticity
EP  - 65
AV  - public
Y1  - 2020///
TI  - Demand Models with Random Partitions
SN  - 0162-1459
UR  - https://doi.org/10.1080/01621459.2019.1604360
A1  - Smith, A
A1  - Allenby, G
JF  - Journal of the American Statistical Association
VL  - 115
SP  - 47
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
IS  - 529
ER  -