Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models
提出一种灵活可扩展的方法，通过自适应稀疏网格插值求解高维随机动态模型，结合混合并行计算提升效率，适用于国际商业周期和多产品菜单成本模型。
We present a flexible and scalable method for computing global solutions of highdimensional stochastic dynamic models.Within a time iteration or value function iteration setup, we interpolate functions using an adaptive sparse grid algorithm.With increasing dimensions, sparse grids grow much more slowly than standard tensor product grids.Moreover, adaptivity adds a second layer of sparsity, as grid points are added only where they are most needed, for instance, in regions with steep gradients or at nondifferentiabilities.To further speed up the solution process, our implementation is fully hybrid parallel, combining distributed and shared memory parallelization paradigms, and thus permits an efficient use of high-performance computing architectures.To demonstrate the broad applicability of our method, we solve two very different types of dynamic models: first, high-dimensional international real business cycle models with capital adjustment costs and irreversible investment; second, multiproduct menu-cost models with temporary sales and economies of scope in price setting.