Large and moderate deviations upper bounds for the Gaussian autoregressive process

Julien Worms

Statistics & Probability Letters, 2001, vol. 51, issue 3, 235-243

Abstract: We study the least-squares estimator in the scalar autoregressive model of order 1 with Gaussian noise and arbitrary fixed initial state. Upper bounds of both large and moderate deviations principles are achieved in the unstable and explosive frameworks. The moderate deviations results are consistent with known results of convergence in distribution of the literature.

Keywords: Autoregressive; models; Least-squares; estimator; Large; and; moderate; deviations; principles (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (3)

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