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Sign tests for dependent observations
File | Description | Size | Format | |
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IbragimovBrownRevisedFinal.pdf | Accepted version | 288.76 kB | Adobe PDF | View/Open |
Title: | Sign tests for dependent observations |
Authors: | Brown, D Ibragimov, R |
Item Type: | Journal Article |
Abstract: | New sign tests for testing equality of conditional distributions of two (arbitrary) adapted processes as well as for testing conditionally symmetric martingale-difference assumptions are introduced. The analysis is based on results that demonstrate that randomization over ties in sign tests for equality of conditional distributions of two adapted sequences produces a stream of i.i.d. symmetric Bernoulli random variables. This reduces the problem of evaluating the critical values of the tests to computing the quantiles or moments of Binomial or normal distributions. Similar properties also hold under randomization over zero values of signs of a conditionally symmetric martingale-difference sequence. |
Issue Date: | 1-Apr-2019 |
Date of Acceptance: | 20-Nov-2018 |
URI: | http://hdl.handle.net/10044/1/67722 |
DOI: | https://doi.org/10.1016/j.ecosta.2018.11.001 |
ISSN: | 2452-3062 |
Publisher: | Elsevier BV |
Start Page: | 1 |
End Page: | 8 |
Journal / Book Title: | Econometrics and Statistics |
Volume: | 10 |
Copyright Statement: | © 2018 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/. |
Sponsor/Funder: | Russian Science Foundation |
Funder's Grant Number: | 16-18-10432 |
Keywords: | Social Sciences Economics Business & Economics Sign tests Dependence Adapted processes Martingale-difference sequences Conditional symmetry Bernoulli random variables Exact tests Conservative tests EXACT NONPARAMETRIC-TESTS RANDOM-WALK LINEAR-COMBINATIONS RANDOM-VARIABLES ORTHOGONALITY VERSIONS DRIFT |
Publication Status: | Published |
Online Publication Date: | 2018-12-21 |
Appears in Collections: | Imperial College Business School |