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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models
Organization Unit
  • Johannes Brumm
  • Simon Scheidegger
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
  • English
Journal Title Econometrica
Publisher The Econometric Society
Geographical Reach international
Volume forthcoming
Page Range -
Date 2017
Abstract Text We present a exible and scalable method for computing global solutions of high- dimensional 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 non-differentiabilities. 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.
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