Header

UZH-Logo

Maintenance Infos

Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models


Brumm, Johannes; Scheidegger, Simon (2017). Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models. Econometrica, 85(5):1575-1612.

Abstract

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.

Abstract

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.

Statistics

Citations

Dimensions.ai Metrics
1 citation in Web of Science®
2 citations in Scopus®
13 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

28 downloads since deposited on 22 Nov 2017
28 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Language:English
Date:September 2017
Deposited On:22 Nov 2017 13:54
Last Modified:19 Feb 2018 09:21
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0012-9682
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3982/ECTA12216
Related URLs:http://onlinelibrary.wiley.com/doi/10.3982/ECTA12216/full (Publisher)
Other Identification Number:merlin-id:14861

Download

Download PDF  'Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models'.
Preview
Content: Published Version
Filetype: PDF
Size: 1MB
View at publisher