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Large-sample inference in the general AR(1) model
| Content Provider | Semantic Scholar |
|---|---|
| Author | Paparoditis, Efstathios Politis, Dimitris N. |
| Abstract | The situation where the available data arise from a general AR(1) model is discussed, and two new avenues for constructing con dence 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 diÆculties 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 nitesample simulations are also presented supporting the applicability of the proposed methods, and the problem of bootstrap block size choice is discussed. AMS 1980 subject classi cations: primary 62M20; secondary 62G05. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.math.ucsd.edu/~politis/PAPER/Ar1TEST.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Automated theorem proving Autoregressive model Block size (cryptography) Booting Cations Inference Naruto Shippuden: Clash of Ninja Revolution 3 Preprocessor Reflections of signals on conducting lines Simulation VHDL-AMS |
| Content Type | Text |
| Resource Type | Article |
