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A Bayesian nonparametric approach to modeling market share dynamics

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  • Igor Prünster
  • Matteo Ruggiero
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    Abstract

    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, 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 Polya 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.

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    Bibliographic Info

    Paper provided by Collegio Carlo Alberto in its series Carlo Alberto Notebooks with number 217.

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    Length: 34 pages
    Date of creation: 2011
    Date of revision:
    Handle: RePEc:cca:wpaper:217

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    Related research

    Keywords: Bayesian Nonparametrics; Gibbs sampler; interacting Polya urns; particle system; species sampling models; market dynamics; interacting Fleming-Viot processes;

    This paper has been announced in the following NEP Reports:

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