Zhang, Z.Q. and Karniadakis, G.E. (2017) Numerical Methods for Stochastic Partial Differential Equations with White Noise. Applied Mathematical Sciences 196. Springer International Publishing AG, Berlin.
https://doi.org/10.1007/978-3-319-57511-7_7

has been cited by the following article:

• TITLE: Review on the Current Stochastic Numerical Methods for Econometric Analysis

  AUTHORS: Lewis N. K. Mambo, Rostin M. M. Mabela, Jean-Pièrre B. Bosonga, Eugène M. Mbuyi

  KEYWORDS: Stochastic Differential Equations, The Euler-Maruyama Scheme, The Milstein Scheme, The Crank-Nicolson Scheme, Runge-Kutta Method, Itô Integrals, Econometric Analysis

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.9 No.4, December 26, 2019

  ABSTRACT: The main aim of this paper is to present and emphasize the contribution of stochastic numerical methods as must tools for the modern econometric modelisation. Indeed, the stochastic numerical methods play an important role in mathematical modelling and the econometric analysis because they model uncertainties that govern the real-world data. However these powerful tools are not well-known and understood by many economists and financial econometricians.