Model Complexity and Restrictiveness
Keaton Ellis, Sara Neff
Abstract
We study the measure of restrictiveness proposed in Fudenberg, Gao, and Liang (2026) to evaluate complexity of economic models using synthetic data. We show that Rademacher complexity is an affine transformation of a particular case of a consistent finite-sample estimate of restrictiveness. Our results show that restrictiveness inherits a cardinal interpretation as a bound on generalization error while avoiding the inability of limiting Rademacher complexity to distinguish between some falsifiable models.
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