Parameter estimation for discretely sampled stochastic heat equation driven by space-only noise

Igor Cialenco and Hyun-Jung Kim

Stochastic Processes and their Applications, 2022, vol. 143, issue C, 1-30

Abstract: We derive consistent and asymptotically normal estimators for the drift and volatility parameters of the stochastic heat equation driven by an additive space-only white noise when the solution is sampled discretely in the physical domain. We consider both the full space and the bounded domain. We establish the exact spatial regularity of the solution, which in turn, using power-variation arguments, allows building the desired estimators. We show that naive approximations of the derivatives appearing in the power-variation based estimators may create nontrivial biases, which we compute explicitly. The proofs are rooted in Malliavin–Stein’s method.

Keywords: Parabolic Anderson model; Quadratic variation; Parameter estimation; Discrete sampling; Space-only noise; Malliavin-Stein’s method (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:143:y:2022:i:c:p:1-30

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DOI: 10.1016/j.spa.2021.09.012

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