Large-sample inference in the general AR(1) model

• Autores: Efstathions Paparoditis, Dimitris N. Politis
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 9, Nº. 2, 2000, págs. 487-509
• Idioma: inglés
• DOI: 10.1007/bf02595747
• Texto completo no disponible (Saber más ...)
• Resumen

  • The situation where the available data arise from a general AR(1) model is discussed, and two new avenues for constructing confidence intervals for the unknown autoregressive root are proposed, one based on a Central Limit Theorem, and the other based on the block-bootstrap. The two new methodologies rely on clever pre-processing of the original series, and are subsequently free of the difficulties present in previous methods that were due to data nonstationarity and/or discontinuity in the limit distribution in the case of a unit root. Some finite-sample simulations are also presented supporting the applicability of the proposed methods, and the problem of bootstrap block size choice is discussed