Adusumilli, Karun, Kurisu, Daisies, Otsu, Taisuke ORCID: 0000-0002-2307-143X and Whang, Yoon-Jae (2020) Inference on distribution functions under measurement error. Journal of Econometrics, 215 (1). 131 - 164. ISSN 0304-4076
Text (Inference on distribution under management error)
- Accepted Version
Download (632kB) |
Abstract
This paper is concerned with inference on the cumulative distribution function (cdf) FX∗ in the classical measurement error model X = X∗ + ε. We consider the case where the density of the measurement error ε is unknown and estimated by repeated measurements, and show validity of a bootstrap approximation for the distribution of the deviation in the sup-norm between the deconvolution cdf estimator and FX∗. We allow the density of ε to be ordinary or super smooth. We also provide several theoretical results on the bootstrap and asymptotic Gumbel approximations of the sup-norm deviation for the case where the density of ε is known. Our approximation results are applicable to various contexts, such as confidence bands for FX∗ and its quantiles, and for performing various cdf-based tests such as goodness-of-fit tests for parametric models of X∗, two sample homogeneity tests, and tests for stochastic dominance. Simulation and real data examples illustrate satisfactory performance of the proposed methods.
Item Type: | Article |
---|---|
Official URL: | https://www.journals.elsevier.com/journal-of-econo... |
Additional Information: | © 2019 Elsevier B.V. |
Divisions: | Economics |
Subjects: | H Social Sciences > HB Economic Theory |
Date Deposited: | 29 Nov 2019 10:33 |
Last Modified: | 01 Nov 2024 05:33 |
URI: | http://eprints.lse.ac.uk/id/eprint/102692 |
Actions (login required)
View Item |