Online parameter estimation for the McKean–Vlasov stochastic differential equation

Louis Sharrock, Nikolas Kantas, Panos Parpas and Grigorios A. Pavliotis

Stochastic Processes and their Applications, 2023, vol. 162, issue C, 481-546

Abstract: We analyse the problem of online parameter estimation for a stochastic McKean–Vlasov equation, and the associated system of weakly interacting particles. We propose an online estimator for the parameters of the McKean–Vlasov SDE, or the interacting particle system, which is based on a continuous-time stochastic gradient ascent scheme with respect to the asymptotic log-likelihood of the interacting particle system. We characterise the asymptotic behaviour of this estimator in the limit as t→∞, and also in the joint limit as t→∞ and N→∞. In these two cases, we obtain almost sure or L1 convergence to the stationary points of a limiting contrast function, under suitable conditions which guarantee ergodicity and uniform-in-time propagation of chaos. We also establish, under the additional condition of global strong concavity, L2 convergence to the unique maximiser of the asymptotic log-likelihood of the McKean–Vlasov SDE, with an asymptotic convergence rate which depends on the learning rate, the number of observations, and the dimension of the non-linear process. Our theoretical results are supported by two numerical examples, a linear mean field model and a stochastic opinion dynamics model.

Keywords: McKean–Vlasov equation; Maximum likelihood; Parameter estimation; Stochastic gradient descent (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spa.2023.05.002

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