An asymptotic theory for sample covariances of Bernoulli shifts
Covariances play a fundamental role in the theory of stationary processes and they can naturally be estimated by sample covariances. There is a well-developed asymptotic theory for sample covariances of linear processes. For nonlinear processes, however, many important problems on their asymptotic behaviors are still unanswered. The paper presents a systematic asymptotic theory for sample covariances of nonlinear time series. Our results are applied to the test of correlations.
Year of publication: |
2009
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Authors: | Wu, Wei Biao |
Published in: |
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 119.2009, 2, p. 453-467
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Publisher: |
Elsevier |
Keywords: | Asymptotic normality Covariance Dependence Linear process Martingale Moderate deviation Nonlinear time series Stationary process Test of correlation |
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