Inference for continuous-time long memory randomly sampled processes

Mohamedou Ould Haye, Anne Philippe () and Caroline Robet
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Mohamedou Ould Haye: Carleton University
Anne Philippe: Nantes Université
Caroline Robet: Nantes Université

Statistical Papers, 2024, vol. 65, issue 5, No 18, 3134 pages

Abstract: Abstract From a continuous-time long memory stochastic process, a discrete-time randomly sampled one is drawn using a renewal sampling process. We establish the existence of the spectral density of the sampled process, and we give its expression in terms of that of the initial process. We also investigate different aspects of the statistical inference on the sampled process. In particular, we obtain asymptotic results for the periodogram, the local Whittle estimator of the memory parameter and the long run variance of partial sums. We mainly focus on Gaussian continuous-time process. The challenge being that the randomly sampled process will no longer be jointly Gaussian.

Keywords: Long memory; Sampled process; Whittle estimator; Periodogram; Spectral density; Limit theorems; Poisson process; Continuous-time Gaussian processes (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01515-z

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