Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

TL;DR

This paper develops and analyzes both offline and online maximum likelihood estimators for parameter estimation in McKean-Vlasov stochastic differential equations, establishing their consistency, asymptotic normality, and convergence properties.

Contribution

It introduces a novel online stochastic gradient ascent method for parameter estimation in McKean-Vlasov SDEs and analyzes its asymptotic behavior.

Findings

Consistency and asymptotic normality of estimators

Convergence to stationary points under ergodicity

L2 convergence with explicit rate under strong concavity

Abstract

We consider the problem of parameter estimation for a stochastic McKean-Vlasov equation, and the associated system of weakly interacting particles. We study two cases: one in which we observe multiple independent trajectories of the McKean-Vlasov SDE, and another in which we observe multiple particles from the interacting particle system. In each case, we begin by establishing consistency and asymptotic normality of the (approximate) offline maximum likelihood estimator, in the limit as the number of observations $N\rightarrow\infty$. We then propose an online maximum likelihood estimator, 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 $t\rightarrow\infty$, and also in the joint limit as $t\rightarrow\infty$ and $N\rightarrow\infty$. In these two cases, we obtain a.s. or $\mathbb{L}_1$ 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, $\mathbb{L}_2$ 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.