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Large-sample inference in the general AR(1) model
Authors:Efstathios Paparoditis  Dimitris N Politis
Affiliation:(1) Department of Mathematics and Statistics, University of Cyprus, Cyprus;(2) Department of Mathematics, University of California, San Diego, 92093-0112 La Jolla, CA, USA
Abstract: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 preprocessing 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. Research partly supported by NSF Grant DMS-97-03964.
Keywords:Bootstrap  confidence intervals  hypothesis tests  resampling  stationarity  unit root
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