A Grid-Rate Condition for Valid Uniform Inference

Fuente: arXiv
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Autore principale: Tsyawo, Emmanuel Selorm
Natura: Preprint
Pubblicazione: 2026
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author Tsyawo, Emmanuel Selorm
author_facet Tsyawo, Emmanuel Selorm
contents Estimating a continuous functional $F: \X \to \R$ involves specifying $L_n^d$ nodes on $\X \subset \R^d$ for estimation and uniform inference. While asymptotically valid inference requires $L_n$ to increase with $n$, existing fixed-$L$ rules of thumb and heuristic data-driven approaches lack formal justification. This paper shows that, for functions within a Donsker class, the simple grid-growth condition \(L_n=ω(r_n^{1/4})\) is sufficient for valid inference for twice continuously differentiable functions estimable at the \(r_n^{1/2}\) rate. This condition ensures that the approximation error is asymptotically negligible relative to the stochastic variation of the empirical process.
format Preprint
id arxiv_https___arxiv_org_abs_2605_12284
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Grid-Rate Condition for Valid Uniform Inference
Tsyawo, Emmanuel Selorm
Econometrics
Estimating a continuous functional $F: \X \to \R$ involves specifying $L_n^d$ nodes on $\X \subset \R^d$ for estimation and uniform inference. While asymptotically valid inference requires $L_n$ to increase with $n$, existing fixed-$L$ rules of thumb and heuristic data-driven approaches lack formal justification. This paper shows that, for functions within a Donsker class, the simple grid-growth condition \(L_n=ω(r_n^{1/4})\) is sufficient for valid inference for twice continuously differentiable functions estimable at the \(r_n^{1/2}\) rate. This condition ensures that the approximation error is asymptotically negligible relative to the stochastic variation of the empirical process.
title A Grid-Rate Condition for Valid Uniform Inference
topic Econometrics
url https://arxiv.org/abs/2605.12284