Estimation of Dynamic Discrete Choice Models in Continuous Time with an Application to Retail Competition
This paper develops a dynamic model of retail competition and uses it to study the impact of the expansion of a new national competitor on the structure of urban markets. In order to accommodate substantial heterogeneity (both observed and unobserved) across agents and markets, the paper first develops a general framework for estimating and solving dynamic discrete choice models in continuous time that is computationally light and readily applicable to dynamic games. In the proposed framework, players face a standard dynamic discrete choice problem at decision times that occur stochastically. The resulting stochastic-sequential structure naturally admits the use of CCP methods for estimation and makes it possible to compute counterfactual simulations for relatively high-dimensional games. The model and method are applied to the retail grocery industry, into which Wal-Mart began rapidly expanding in the early 1990s, eventually attaining a dominant position. We find that Wal-Mart’s expansion into groceries came mostly at the expense of the large incumbent supermarket chains, rather than the single-store outlets that bore the brunt of its earlier conquest of the broader general merchandise sector. Instead, we find that independent grocers actually thrive when Wal-Mart enters, leading to an overall reduction in market concentration. These competitive effects are strongest in larger markets and those into which Wal-Mart expanded most rapidly, suggesting a diminishing role of scale and a greater emphasis on differentiation in this previously mature industry.
Document Object Identifier (DOI): 10.3386/w18449
Published: Peter Arcidiacono & Patrick Bayer & Jason R. Blevins & Paul B. Ellickson, 2016. "Estimation of Dynamic Discrete Choice Models in Continuous Time with an Application to Retail Competition," The Review of Economic Studies, vol 83(3), pages 889-931. citation courtesy of
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