Inference in generalized exponential O–U processes with change-point

Yunhong Lyu () and Sévérien Nkurunziza ()
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Yunhong Lyu: University of Windsor
Sévérien Nkurunziza: University of Windsor

Statistical Inference for Stochastic Processes, 2024, vol. 27, issue 1, No 3, 63-102

Abstract: Abstract In this paper, we consider an inference problem in generalized exponential Ornstein–Uhlenbeck processes with change-point in the context where the dimensions of the drift parameter are unknown. The proposed method generalizes the work in recent literature for which the change-point has never been considered. Thus, in addition to taking care of possible chock, we study the asymptotic properties of the unrestricted estimator, the restricted estimator, and shrinkage estimators for the drift parameters. We also derive an asymptotic test for change-point detection and we establish the asymptotic distributional risk of the proposed estimators as well as their relative efficiency. Further, we prove that the proposed methods improve the goodness-of-fit. Finally, we present the simulation results which corroborate the theoretical findings and we analyze a financial market data set.

Keywords: Asymptotic normality; Change-point detection; Drift parameter; GEOU process; Random Dimensions; Relative efficiency (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11203-023-09293-z

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