Author(s)

Lewis N. K. Mambo^1,2, Rostin M. M. Mabela³, Jean-Pièrre B. Bosonga¹, Eugène M. Mbuyi³

¹Department of Economics, University of Kinshasa, Kinshasa, Congo.
²Central Bank of Congo, Kinshasa, Congo.
³Department of Mathematics and Computer Sciences, University of Kinshasa, Kinshasa, Congo.

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.

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

Mambo, L. , Mabela, R. , Bosonga, J. and Mbuyi, E. (2019) Review on the Current Stochastic Numerical Methods for Econometric Analysis. American Journal of Computational Mathematics, 9, 328-347. doi: 10.4236/ajcm.2019.94024.