Robust unit root tests for autoregressive models : Notas de Matemática, 58

In this paper a robust test is developed for detecting a unit root for autoregressive models. The basic idea consists of replacing the least squares estimators in the Dickey-Fuller statistics by robust estimators with a high breakdown point and high efficiency called τ-estimators. The limiting distr...

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Autor principal: Ferretti, Nélida Elena
Formato: Publicacion seriada
Lenguaje:Español
Publicado: 1996
Materias:
Matemática
Autoregressive processes
Empirical power
Robust estimation
Unit root test
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/170676
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-170676
record_format dspace
spelling I19-R120-10915-1706762024-09-25T20:10:48Z http://sedici.unlp.edu.ar/handle/10915/170676 Robust unit root tests for autoregressive models : Notas de Matemática, 58 Ferretti, Nélida Elena 1996 2024-09-25T19:28:49Z es Matemática Autoregressive processes Empirical power Robust estimation Unit root test In this paper a robust test is developed for detecting a unit root for autoregressive models. The basic idea consists of replacing the least squares estimators in the Dickey-Fuller statistics by robust estimators with a high breakdown point and high efficiency called τ-estimators. The limiting distribution of the test statistics proposed are obtained under the unit root null hypothesis. A Monte Carlo study is described, illustring the asymptotic efficiency of the τ-estimators and empirical power comparisons using moderate and large size samples for first-order autoregressive processes. The new tests are shown to have the desirable robust properties. Material digitalizado en SEDICI gracias a la colaboración de la Biblioteca del Departamento de Matemática de la Facultad de Ciencias Exactas (UNLP). Facultad de Ciencias Exactas Publicacion seriada Publicacion seriada http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Matemática
Autoregressive processes
Empirical power
Robust estimation
Unit root test
spellingShingle Matemática
Autoregressive processes
Empirical power
Robust estimation
Unit root test
Ferretti, Nélida Elena
Robust unit root tests for autoregressive models : Notas de Matemática, 58
topic_facet Matemática
Autoregressive processes
Empirical power
Robust estimation
Unit root test
description In this paper a robust test is developed for detecting a unit root for autoregressive models. The basic idea consists of replacing the least squares estimators in the Dickey-Fuller statistics by robust estimators with a high breakdown point and high efficiency called τ-estimators. The limiting distribution of the test statistics proposed are obtained under the unit root null hypothesis. A Monte Carlo study is described, illustring the asymptotic efficiency of the τ-estimators and empirical power comparisons using moderate and large size samples for first-order autoregressive processes. The new tests are shown to have the desirable robust properties.
format Publicacion seriada
Publicacion seriada
author Ferretti, Nélida Elena
author_facet Ferretti, Nélida Elena
author_sort Ferretti, Nélida Elena
title Robust unit root tests for autoregressive models : Notas de Matemática, 58
title_short Robust unit root tests for autoregressive models : Notas de Matemática, 58
title_full Robust unit root tests for autoregressive models : Notas de Matemática, 58
title_fullStr Robust unit root tests for autoregressive models : Notas de Matemática, 58
title_full_unstemmed Robust unit root tests for autoregressive models : Notas de Matemática, 58
title_sort robust unit root tests for autoregressive models : notas de matemática, 58
publishDate 1996
url http://sedici.unlp.edu.ar/handle/10915/170676
work_keys_str_mv AT ferrettinelidaelena robustunitroottestsforautoregressivemodelsnotasdematematica58
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