Parameter inference for degenerate diffusion processes

Yuga Iguchi, Alexandros Beskos and Matthew M. Graham

Stochastic Processes and their Applications, 2024, vol. 174, issue C

Abstract: We study parametric inference for ergodic diffusion processes with a degenerate diffusion matrix. Existing research focuses on a particular class of hypo-elliptic Stochastic Differential Equations (SDEs), with components split into ‘rough’/‘smooth’ and noise from rough components propagating directly onto smooth ones, but some critical model classes arising in applications have yet to be explored. We aim to cover this gap, thus analyse the highly degenerate class of SDEs, where components split into further sub-groups. Such models include e.g. the notable case of generalised Langevin equations. We propose a tailored time-discretisation scheme and provide asymptotic results supporting our scheme in the context of high-frequency, full observations. The proposed discretisation scheme is applicable in much more general data regimes and is shown to overcome biases via simulation studies also in the practical case when only a smooth component is observed. Joint consideration of our study for highly degenerate SDEs and existing research provides a general ‘recipe’ for the development of time-discretisation schemes to be used within statistical methods for general classes of hypo-elliptic SDEs.

Keywords: Stochastic differential equation; Hypo-elliptic diffusion; Hörmander’s condition; Partial observations; Generalised Langevin equation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.spa.2024.104384

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