We propose a restrictiveness measure for economic models based on how well they fit synthetic data from a pre-defined class. This measure, together with a measure for how well the model fits real data, outlines a Pareto frontier, where models that rule out more regularities, yet capture the regularities that are present in real data, are preferred. To illustrate our approach, we evaluate the restrictiveness of popular models in two laboratory settings -- certainty equivalents and initial play -- and in one field setting -- takeup of microfinance in Indian villages. The restrictiveness measure reveals new insights about each of the models, including that some economic models with only a few parameters are very flexible.

翻译：我们提出了一种基于模型对预定义类别合成数据拟合程度的经济模型限制性度量方法。该度量与模型对真实数据拟合程度的度量相结合，勾画出一条帕累托边界，其中排除了更多规律性却能捕捉真实数据中存在规律性的模型更受青睐。为阐释该方法，我们在两个实验室场景（确定性等价与初始博弈）和一个实地场景（印度村庄小额信贷的采用）中评估了流行模型的限制性。该限制性度量揭示了每个模型的新见解，包括一些仅含少量参数的经济模型具有高度的灵活性。