Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models.
We present a flexible 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...
| Publicado en: | Econometrica Vol. 85; no. 5; pp. 1575 - 1613 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=125422940&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 125422940 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Sep2017 vid: 85 iid: 5 pid: 480 pub: Wiley-Blackwell artinfo: ui: 125422940 10.3982/ECTA12216 ppf: 1575 ppct: 38 formats: tig: atl: Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models. aug: au: Brumm, Johannes Scheidegger, Simon affil: Department of Economics and Management, Karlsruhe Institute of Technology Department of Banking and Finance, University of Zurich su: Stochastic analysis Mathematical functions Algorithms Mathematical analysis Algebra sug: subj: Stochastic analysis Mathematical functions Algorithms Mathematical analysis Algebra keyword: Adaptive sparse grids high-performance computing international real business cycles menu costs occasionally binding constraints Adaptive sparse grids high-performance computing international real business cycles menu costs occasionally binding constraints ab: We present a flexible 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 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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