Nonparametric calibration for stochastic reaction–diffusion equations based on discrete observations

Florian Hildebrandt and Mathias Trabs

Stochastic Processes and their Applications, 2023, vol. 162, issue C, 171-217

Abstract: Nonparametric estimation for semilinear SPDEs, namely stochastic reaction–diffusion equations in one space dimension, is studied. We consider observations of the solution field on a discrete grid in time and space with infill asymptotics in both coordinates. Firstly, we derive a nonparametric estimator for the reaction function of the underlying equation. The estimate is chosen from a finite-dimensional function space based on a least squares criterion. Oracle inequalities provide conditions for the estimator to achieve the usual nonparametric rate of convergence. Adaptivity is provided via model selection. Secondly, we show that the asymptotic properties of realized quadratic variation based estimators for the diffusivity and volatility carry over from linear SPDEs. In particular, we obtain a rate-optimal joint estimator of the two parameters. The result relies on our precise analysis of the Hölder regularity of the solution process and its nonlinear component, which may be of its own interest. Both steps of the calibration can be carried out simultaneously without prior knowledge of the parameters.

Keywords: Infill asymptotics; Realized quadratic variation; Model selection; Semilinear stochastic partial differential equations (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.spa.2023.04.019

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