Inference in the presence of unknown rates

Hao Dong, Taisuke Otsu and Luke Taylor

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The convergence rate of an estimator can vary when applied to datasets from different populations. As the population is unknown in practice, so is the corresponding convergence rate. In this article, we introduce a method to conduct inference on estimators whose convergence rates are unknown. Specifically, we extend the subsampling approach of Bertail, Politis, and Romano (1999) to situations where the convergence rate may include logarithmic components. This extension proves to be particularly relevant in certain statistical inference problems. To illustrate the practical relevance and implementation of our results, we discuss two main examples: (i) non parametric regression with measurement error; and (ii) intercept estimation in binary choice models. In each case, our approach provides robust inference in settings where convergence rates are unknown; simulation results validate our findings.

Keywords: Binary choice; convergence rate; measurement error; subsampling (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2024-12-24
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Published in Econometric Reviews, 24, December, 2024. ISSN: 0747-4938

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