Frequency Domain Tests for Assessing Dependency Characteristics of Stationary Time Series via Tapering
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We present a handful of periodogram-based test statistics underneath two statistical procedures that assess the characteristics of a stationary time series in a manner that is similar to the Gromykov et al.(2018) and Ould Haye & Phillippe (2019). We incorporate tapering into these statistics by utilizing a cosine bell taper and a new class of tapers which we term as "random tapers". In the first procedure we test for short memory versus long memory. In the second we test for dependence against the presence of a trend. We demonstrate that under the null, both procedures' limiting distributions are easily obtainable and follow Gamma-like distributions. Moreover, we evaluate the P-value and Empirical Power plots accrued by each test and find that they yield precise results of empirical size. The tests are also implemented into two sets of realworld data and the accrued results are presented.
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Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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barski-frequencydomaintestsforassessingdependency.pdf | 2023-05-05 | Public | Download |