Computing equilibrium in OLG models with stochastic production

Krueger, Dirk; Kübler, Felix

doi:10.1016/s0165-1889(03)00111-8

Cited by 147 publications

(158 citation statements)

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“…Economists have recently begun to explore sparse grids (Smolyak, 1963;Krueger and Kubler, 2004;Judd et al, 2014b;Rudik, 2016). Sparse grids are constructed similarly to the standard tensor product grid; however, they omit certain subspaces of the full tensor product grid that are less important for approximation quality.…”

Section: Uncertainty and Climate Changementioning

confidence: 99%

Managing Climate Change Under Uncertainty: Recursive Integrated Assessment at an Inflection Point

Lemoine

Rudik

2017

Annu. Rev. Resour. Econ.

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Uncertainty is critical to questions about climate change policy. Recently developed recursive integrated assessment models have become the primary tools for studying and quantifying the policy implications of uncertainty. We decompose the channels through which uncertainty affects policy and quantify them in a recursive extension of a benchmark integrated assessment model. The first wave of recursive models has made valuable, pioneering efforts at analyzing disparate sources of uncertainty. We argue that frontier numerical methods will enable the next generation of recursive models to better capture the information structure of climate change and to thereby ask new types of questions about climate change policy.

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Section: Uncertainty and Climate Changementioning

confidence: 99%

Managing Climate Change Under Uncertainty: Recursive Integrated Assessment at an Inflection Point

Lemoine

Rudik

2017

Annu. Rev. Resour. Econ.

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“…Model-based economics has for the most part reacted to this challenge in two ways. Either by focusing on qualitative results obtained from extremely simplified models with little heterogene- [3] analyze the welfare implications of social security reform in a model where one period corresponds to six years, thereby reducing the number of adult cohorts and thus the dimensionality of the problem by a factor of six. 20 Similarly, international real business cycle (IRBC) models often include only a very small number of countries or regions.…”

Section: Introductionmentioning

confidence: 99%

“…This scheme enables us to make efficient use of modern hybrid high-performance computing facilities, whose performance nowadays reaches multiple petaflops [12]. Their hybrid architecture typically features CPU compute nodes with attached GPUs 3 . We show 50 in this paper that the generic structure of an algorithm that solves dynamic economic models by time iteration or dynamic programming using sparse grids is a natural match for such hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

“…We show 50 in this paper that the generic structure of an algorithm that solves dynamic economic models by time iteration or dynamic programming using sparse grids is a natural match for such hybrid systems. 3 The Swiss National Supercomputer Centre's "Piz Daint" Cray XC30 that is used in the numerical experiments in Sec. 5 consists of Intel Xeon E5 processors with NVIDIA Tesla K20X GPUs attached to it; its peak performance is 7.7 petaflops.…”

Section: Introductionmentioning

confidence: 99%

“…Sparse grids go back to Smolyak [14] and have been applied to a whole range of different research fields such as physics, visualization, data mining, Hamilton-Jacobi Bellman (HJB) equations, mathematical finance, insurance, and econometrics [15,16,8,17,18,19,20,21,22]. Krueger and Kubler [3] and Judd et al [23] solve dynamic economic models using sparse grids with global polynomials 65 as basis functions. In contrast, we use piecewise-linear local basis functions first introduced by Zenger [24] in the context of sparse grids.…”

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confidence: 99%

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Scalable high-dimensional dynamic stochastic economic modeling

Brumm

Mikushin

Scheidegger

et al. 2015

Journal of Computational Science

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We present a highly parallelizable and flexible computational method to solve high-dimensional stochastic dynamic economic models. Solving such models often requires the use of iterative methods, like time iteration or dynamic programming. By exploiting the generic iterative structure of this broad class of economic problems, we propose a parallelization scheme that favors hybrid massively parallel computer architectures. Within a parallel nonlinear time iteration framework, we interpolate policy functions partially on GPUs using an adaptive sparse grid algorithm with piecewise linear hierarchical basis functions. GPUs accelerate this part of the computation one order of magnitude thus reducing overall computation time by 50%. The developments in this paper include the use of a fully adaptive sparse grid algorithm and the use of a mixed MPI-Intel TBB-CUDA/Thrust implementation to improve the interprocess communication strategy on massively parallel architectures. Numerical experiments on "Piz Daint" (Cray XC30) at the Swiss National Supercomputing Centre show that high-dimensional international real business cycle models can be efficiently solved in parallel. To our knowledge, this performance on a massively parallel petascale architecture for such nonlinear high-dimensional economic models has not been possible prior to present work. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractWe present a highly parallelizable and flexible computational method to solve highdimensional stochastic dynamic economic models. Solving such models often requires the use of iterative methods, like time iteration or dynamic programming. By exploiting the generic iterative structure of this broad class of economic problems, we propose a parallelization scheme that favors hybrid massively parallel computer architectures.Within a parallel nonlinear time iteration framework, we interpolate policy functions partially on GPUs using an adaptive sparse grid algorithm with piecewise linear hierarchical basis functions. GPUs accelerate this part of the computation one order of magnitude thus reducing overall computation time by 50%. The developments in this paper include the use of a fully adaptive sparse grid algorithm and the use of a mixed MPI-Intel TBB-CUDA/Thrust implementation to improve the interprocess communication strategy on massively parallel architectures. Numerical experiments on "Piz Daint" (Cray XC30) at the Swiss National Supercomputing Centre show that highdimensional international real business cycle models can be efficiently solved in parallel. To our knowledge, this performance on a massively ...

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