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Bayesian Analysis of Partitioned Demand Models

Smith, Adam Nicholas
http://rave.ohiolink.edu/etdc/view?acc_num=osu1497895561381294

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Business Administration.
Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in empirical settings in which the product space is large or spans multiple categories. While partitioning demand can offer both statistical and behavioral advantages, doing so can also impose significant restrictions on the set of admissible substitution patterns. In this dissertation, we let the partition be a model parameter and propose a Bayesian method for inference. The challenge in modeling partitions is that they are defined on a high-dimensional, discrete, and non-Euclidean domain. We build on previous nonparametric Bayesian models for random partitions to construct a new partition distribution characterized by a location partition and scale parameter. This location-scale partition distribution is useful in two ways: (1) as a proposal distribution within the context of a Markov chain Monte Carlo routine; and (2) as a prior or random-effects distribution of partition heterogeneity. Our method is illustrated in the context of both store-level and household-level demand models. We find that allowing for uncertainty in the partition is important for preserving model flexibility, improving demand forecasts, learning about the structure of demand, and informing targeted marketing strategies.
Greg Allenby (Advisor)
Nino Hardt (Committee Member)
Mingyu Joo (Committee Member)
Peter Rossi (Committee Member)
105 p.
Economics; Marketing; Statistics
random partition models; Polya urn; Markov chain Monte Carlo; high-dimensional demand; price elasticities; heterogeneity; hierarchical models; price promotions; targeted marketing

Recommended Citations

Citations

  • Smith, A. N. (2017). Bayesian Analysis of Partitioned Demand Models [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497895561381294

    APA Style (7th edition)

  • Smith, Adam. Bayesian Analysis of Partitioned Demand Models. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1497895561381294.

    MLA Style (8th edition)

  • Smith, Adam. "Bayesian Analysis of Partitioned Demand Models." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497895561381294

    Chicago Manual of Style (17th edition)

osu1497895561381294
408
© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.