Thin Sets Are Not Equally Thin: Minimax Learning of Submanifold Integrals

Many economic parameters are identified by “thin sets” (submanifolds with Lebesgue measure zero) and hence difficult to recover from data in an ambient space. This paper provides a unified theory for estimation and inference of such “thin-set” identified functionals. We show that thin sets are not equally thin: their intrinsic dimensionality m matters in a precise manner. For a nonparametric regression h₀ with Hölder smoothness s and d-dimensional covariates in the ambient space, we show that$\mathrm{ n}—\frac{s}{2s\mathrm{+}d\mathrm{-}m}$, where s is the minimax optimal rate of estimating linear and nonlinear (e.g., quadratic, upper contour) integrals of h₀ on an m-dimensional submanifold (0 ≤ m < d), which is the fastest possible attainable rate among all estimators. The minimax lower bound rate result is generalized to estimating submanifold integrals when h₀ is a nonparametric density and a nonparametric instrumental variable function. The asymptotic normality of t statistics is established via sieve Riesz representation, and the corresponding inference is computed using Sobol points.