Open Access
Translator Disclaimer
February 2013 A Bayesian nonparametric approach to modeling market share dynamics
Igor Prünster, Matteo Ruggiero
Bernoulli 19(1): 64-92 (February 2013). DOI: 10.3150/11-BEJ392


We propose a flexible stochastic framework for modeling the market share dynamics over time in a multiple markets setting, where firms interact within and between markets. Firms undergo stochastic idiosyncratic shocks, which contract their shares, and compete to consolidate their position by acquiring new ones in both the market where they operate and in new markets. The model parameters can meaningfully account for phenomena such as barriers to entry and exit, fixed and sunk costs, costs of expanding to new sectors with different technologies and competitive advantage among firms. The construction is obtained in a Bayesian framework by means of a collection of nonparametric hierarchical mixtures, which induce the dependence between markets and provide a generalization of the Blackwell–MacQueen Pólya urn scheme, which in turn is used to generate a partially exchangeable dynamical particle system. A Markov Chain Monte Carlo algorithm is provided for simulating trajectories of the system, by means of which we perform a simulation study for transitions to different economic regimes. Moreover, it is shown that the infinite-dimensional properties of the system, when appropriately transformed and rescaled, are those of a collection of interacting Fleming–Viot diffusions.


Download Citation

Igor Prünster. Matteo Ruggiero. "A Bayesian nonparametric approach to modeling market share dynamics." Bernoulli 19 (1) 64 - 92, February 2013.


Published: February 2013
First available in Project Euclid: 18 January 2013

zbMATH: 1288.62042
MathSciNet: MR3019486
Digital Object Identifier: 10.3150/11-BEJ392

Keywords: Bayesian nonparametrics , Gibbs sampler , interacting Fleming–Viot processes , interacting Pòlya urns , market dynamics , Particle system , species sampling models

Rights: Copyright © 2013 Bernoulli Society for Mathematical Statistics and Probability


Vol.19 • No. 1 • February 2013
Back to Top