Estimation of Discrete Choice Models with Many Alternatives Using Random Subsets of the Full Choice Set: With an Application to Demand for Frozen Pizza
Estimation of Discrete Choice Models with Many Alternatives Using Random Subsets of the Full Choice Set: With an Application to Demand for Frozen Pizza
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使用完整选择集的随机子集估计具有多种选择的离散选择模型：应用于冷冻比萨饼的需求
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发表时间：
2012
期刊：
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通讯作者：
M. Keane
中科院分区：
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作者：
Nada Wasi;M. Keane
离散选择模型估计中的一个常见问题是完整的选择集非常大。一个很好的例子是超市消费品，例如早餐麦片，通常有一百种或更多品种（SKU 或 UPC）可供选择。在这种情况下，选择概率没有封闭形式的复杂离散选择模型的估计可能在计算上非常繁重。我们展示了使用完整选择集的随机子集如何成为减少计算负担的有用手段。我们应用这种方法来估算冷冻披萨的需求，冷冻披萨有近 100 个品种可供选择。我们提供了一些有趣的新结果，说明特定品种的价格变化如何导致品牌内的品种切换与品牌切换。特别是，当某个品种价格上涨时，大多数人转向其他品牌，而不是同一品牌的其他品种。
A common problem in estimation of discrete choice models is that the complete choice set is very large. A good example is supermarket consumer goods, like breakfast cereal, where there are often a hundred or more varieties (SKUs or UPCs) to choose from. In that case, estimation of complex discrete choice models where choice probabilities have no closed form can be very computationally burdensome. We show how use of random subsets of the full choice set can be a useful device to reduce computational burden. We apply this approach to estimating demand for frozen pizza, where there are nearly 100 varieties to choose from. We provide some interesting new results on how price changes for a particular variety of a brand lead to variety switching within the brand vs. brand switching. In particular, when a variety raises its price, most switching is to other brands, rather than to other varieties of the same brand.