We propose a novel measure of goodness of t for stochastic choice models: that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best speci cation of the model and another representing residual behavior. We claim that the three elements involved in a separation are instrumental to understanding the data. We show how to apply our approach to any stochastic choice model and then study the case of four well-known models, each capturing a different notion of randomness. We illustrate our results with an experimental dataset. (JEL: D00)

中文翻译：

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将预测的随机性与残留行为分开

我们提出了一种用于随机选择模型的t优度的新颖度量：即，可以与模型协调的最大数据部分。该过程是将数据分为两部分：一个是由模型的最佳规范生成的，另一个是表示残留行为的部分。我们认为，分离中涉及的三个要素有助于理解数据。我们展示了如何将我们的方法应用于任何随机选择模型，然后研究四个众所周知的模型的情况，每个模型都捕获了不同的随机性概念。我们用实验数据集说明了我们的结果。（JEL：D00）