  We propose a scalable method for computing global solutions of nonlinear,
high-dimensional dynamic stochastic economic models. First, within a time
iteration framework, we approximate economic policy functions using an
adaptive, high-dimensional model representation scheme, combined with adaptive
sparse grids to address the ubiquitous challenge of the curse of
dimensionality. Moreover, the adaptivity within the individual component
functions increases sparsity since grid points are added only where they are
most needed, that is, in regions with steep gradients or at
nondifferentiabilities. Second, we introduce a performant vectorization scheme
for the interpolation compute kernel. Third, the algorithm is hybrid
parallelized, leveraging both distributed- and shared-memory architectures. We
observe significant speedups over the state-of-the-art techniques, and almost
ideal strong scaling up to at least $1,000$ compute nodes of a Cray XC$50$
system at the Swiss National Supercomputing Center. Finally, to demonstrate our
method's broad applicability, we compute global solutions to two variates of a
high-dimensional international real business cycle model up to $300$ continuous
state variables. In addition, we highlight a complementary advantage of the
framework, which allows for a priori analysis of the model complexity.
