AI 中文总结
本文提出基于最小描述长度的数据压缩方法评估经济模型，统一确定性与随机模型，改进模型选择，模拟与实证显示其优于AIC等准则，可优化模型排名。
AI 中文摘要
比较经济模型需要在拟合度与灵活性之间进行权衡。然而，标准选择准则常以参数数量代理灵活性，忽略了函数形式和实验设计带来的差异。本文引入一种评估经济模型的数据压缩方法，遵循最小描述长度（Minimum Description Length）原理，将模型视为编码，通过其压缩数据的有效性进行评估。该视角产生了塞尔滕（Selten）预测成功的压缩类似物，以及弗登伯格（Fudenberg）等人的完备性和限制性的压缩类似物。与塞尔滕的测度不同，其类似物在似然框架中统一了确定性和随机模型；与弗登伯格等人的测度不同，所提框架提供了完整的选择准则。该方法在社会偏好、风险选择和跨期选择中的应用体现了其相关性。在模拟中，考虑函数形式和实验设计带来的灵活性差异，相比AIC、BIC和交叉验证，提升了模型恢复效果；在实证再分析中，最小描述长度（MDL）改变了模型排名，倾向于更简约的设定。
英文摘要
Comparing economic models requires balancing fit against flexibility. Yet standard selection criteria often proxy flexibility using parameter counts, overlooking differences due to functional form and experimental design. This paper introduces a data compression approach for evaluating economic models. Following the Minimum Description Length principle, models are interpreted as codes and evaluated by how effectively they compress data. This perspective yields compression-based analogues of Selten's predictive success and Fudenberg et al.'s completeness and restrictiveness. Unlike Selten's measure, its analogue unifies deterministic and stochastic models in a likelihood framework; unlike Fudenberg et al.'s measures, the proposed framework provides a complete selection criterion. Applications to social preferences, risky choice, and intertemporal choice illustrate the relevance of the approach. In simulations, accounting for flexibility differences due to functional form and experimental design improves model recovery relative to AIC, BIC, and cross-validation. In empirical reanalyses, MDL changes model rankings in favor of more parsimonious specifications.
Comments63 pages, 11 figures, 8 tables; includes a 24-page Online Appendix
