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- We aggregate stores according to Dominick’s pricing zones. See Hellerstein (2008) for details. At the UPC-week level, we define an â€Âon-sale†dummy that is equal to unity if and only if any sort of promotional activity was recorded in the Dominicks’ data for that UPC and week. Then, when aggregating the data across stores, weeks, and UPCs, we take the mean value of this dummy as our product-level measure of sales/promotional activity. The â€Ânumber of products on sale†instrumental variable is the sum of our product-level sales/promotion measure over products within a market.
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Wolfram, C. D. (1999). Measuring duopoly power in the british electricity spot market. American Economic Review, 805–826. A Appendix A.1 Demand estimation In the terminology of BLP, the nonlinear parameters for our models are βp, Ãp, and Ãl. We search over these parameters using a global optimization algorithm initially (differential evolution), and then we switch to Nelder-Meade. A.2 Demand data We aggregate demand over time and products. We define a product as all UPC codes that share a volume, value for the light beer dummy, and brand. The revenues and quantities (in units of six packs) are added up across such UPC codes for each month, and then prices are recovered by dividing revenues by quantities.