Although almost nonexistent 15 years ago, there are now numerous papers that analyze
models with both aggregate uncertainty and a large number—typically a continuum—of …

We develop numerically stable and accurate stochastic simulation approaches for solving
dynamic economic models. First, instead of standard least‐squares approximation methods …

We introduce a numerical algorithm for solving dynamic economic models that merges
stochastic simulation and projection approaches: we use simulation to approximate the …

We survey numerical methods that are tractable in dynamic economic models with a finite,
large number of continuous state variables.(Examples of such models are new Keynesian …

We describe a sparse-grid collocation method to compute recursive solutions of dynamic
economies with a sizable number of state variables. We show how powerful this method can …

We perform a comparison of Matlab, Python and Julia as programming languages to be
used for implementing global nonlinear solution techniques. We consider two popular …

We use the stochastic simulation algorithm, described in Judd et al.(2009), and the cluster-
grid algorithm, developed in Judd et al.(2010a), to solve a collection of multi-country real …

We develop an envelope condition method (ECM) for dynamic programming problems–a
tractable alternative to expensive conventional value function iteration (VFI). ECM has two …

We compare the performance of perturbation, projection, and stochastic simulation
algorithms for solving the multi-country RBC model described in Den Haan et al.(this issue) …

This paper develops a novel approach for estimating latent state variables of Dynamic
Stochastic General Equilibrium (DSGE) models that are solved using a second-order …