Inference in the presence of unknown rates

Dong, Hao, Otsu, Taisuke ORCID: 0000-0002-2307-143X and Taylor, Luke (2024) Inference in the presence of unknown rates. Econometric Reviews. ISSN 0747-4938 (In Press)

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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 paper, 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) nonparametric 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. 1. Introduction

Item Type:	Article
Additional Information:	© 2024
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
Date Deposited:	13 Nov 2024 16:57
Last Modified:	13 Nov 2024 17:39
URI:	http://eprints.lse.ac.uk/id/eprint/126066

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