We present a flexible and scalable method for computing global solutions of highdimensional 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.

We develop a generic and transparent calibration strategy for simple climate models used in economics. The goal is to choose the free model parameters such as to best match the output of large-scale Earth System Models from the Coupled Model Intercomparison Project, run on pre-defined emissions scenarios. We propose to jointly use four different test cases that are considered pivotal in the climate science literature: two highly idealized tests to separately examine the carbon cycle and the temperature response, and two tests closer to real scenarios, incorporating gradual changes in CO2 emissions and exogenous forcings. To illustrate the applicability of our method, we re-calibrate the free parameters of the climate part of the seminal DICE-2016 model for three different CMIP5 model responses: the multi-model mean as well as two CMIP5 models that exhibit extreme but still permissible equilibrium climate sensitivities. As an additional novelty, our calibrations of DICE-2016 allow for an arbitrary time step in the model explicitly. By applying our comprehensive suite of tests, we i) confirm that both the temperature equations and the carbon cycle in DICE-2016 are miscalibrated and ii) we show that by re-calibrating coefficients all CMIP5 targets considered can be well matched. Finally, we apply the economic model from DICE-2016 in combination with the newly calibrated climate model to compute the social cost of carbon and optimal warming. We find the social cost of carbon to be similar to DICE-2016, while the optimal long-run temperature is almost one degree lower. The social cost of carbon turns out to be much less sensitive to the discount rate than in DICE-2016. We explain how the model's climate part relates to these differences. As the temperature in DICE-2016 under optimal mitigation falls outside the range of CMIP5 projections, we caution that one might want to be skeptical about policy advice based on DICE-2016.