Article citationsMore>>

Duffy, J. and McNelis, P.D. (2002) Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm. Journal of Economic Dynamics and Control, 25, 1273-1303.
https://doi.org/10.1016/S0165-1889(99)00077-9

has been cited by the following article:

• TITLE: Projection Methods and the Curse of Dimensionality

  AUTHORS: Burkhard Heer, Alfred Maußner

  KEYWORDS: Projection Methods, Galerkin, Collocation, Least Squares, Heterogeneous Agents, Stochastic Dynamic General Equilibrium

  JOURNAL NAME: Journal of Mathematical Finance, Vol.8 No.2, May 8, 2018

  ABSTRACT: We study the ability of three different projection methods to solve high-dimensional state space problems: Galerkin, collocation, and least squares projection. The curse of dimensionality can be reduced substantially for both Least Squares and Galerkin projection methods through the use of monomial formulas. Least Squares are shown to require a good initial value in order to give an accurate solution. Alternatively, we suggest a new ad hoc collocation method for complete polynomials that is fast and easy to implement.