Nonparametric Estimation in SDE Models Involving an Explanatory Process

TL;DR

This paper introduces a nonparametric estimation method for SDE models involving an exogenous process, establishing existence, uniqueness, and density properties, along with a convergence rate for the proposed estimator.

Contribution

It provides a new nonparametric estimation framework for SDEs with exogenous processes, including theoretical guarantees and a practical model selection procedure.

Findings

Proved existence and uniqueness of solutions for the SDE model.

Established convergence rates for the estimator in Sobolev spaces.

Developed a model selection method balancing bias and variance.

Abstract

This paper deals with the process $X = (X_t)_{t\in [0,T]}$ defined by the stochastic differential equation (SDE) $dX_t = (a(X_t) + b(Y_t))dt +\sigma(X_t)dW_1(t)$, where $W_1$ is a Brownian motion and $Y$ is an exogenous process. The first task - of probabilistic nature - is to properly define the model, to prove the existence and uniqueness of the solution of such an equation, and then to establish the existence and a suitable control of a density with respect to the Lebesgue measure of the distribution of $(X_t,Y_t)$ ($t > 0$). In the second part of the paper, a risk bound and a rate of convergence in specific Sobolev spaces are established for a copies-based projection least squares estimator of the $\mathbb R^2$-valued function $(a,b)$. Moreover, a model selection procedure making the adequate bias-variance compromise both in theory and practice is investigated.