Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models

We present a finite difference method working on sparse grids to solve higher dimensional heterogeneous agent models. If one wants to solve the arising Hamilton-Jacobi-Bellman equation on a standard full grid, one faces the problem that the number of grid points grows exponentially with the number of dimensions. Discretizations on sparse grids only involve O(N(logN)d-1) degrees of freedom in comparison to the O(Nd) degrees of freedom of conventional methods, where N denotes the number of grid points in one coordinate direction and d is the dimension of the problem. While one can show convergence for the used finite difference method on full grids by using the theory introduced by Barles and Souganidis, we explain why one cannot simply use their results for sparse grids. Our numerical studies show that our method converges to the full grid solution for a two-dimensional model. We analyze the convergence behavior for higher dimensional models and experiment with different sparse grid adaptivity types.