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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| Formato: | Publicacion seriada |
| Lenguaje: | Español |
| Publicado: |
1996
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/170676 |
| Aporte de: |
| Sumario: | 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. |
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