close as possible to moments of the true data. This paper uses importance sampling techniques to address a significant …

In conventional stochastic simulation algorithms, Monte Carlo integration and curve fitting are merged together and … stochastic simulation approach in which integration and curve fitting are separated. We specifically allow for the use of … achieve accuracy of solutions that is orders of magnitude higher than that of the conventional stochastic simulation …

We introduce an algorithm for solving dynamic economic models that merges stochastic simulation and projection … approaches: we use simulation to approximate the ergodic measure of the solution, we construct a fixed grid covering the support …

We develop numerically stable stochastic simulation approaches for solving dynamic economic models. We rely on standard … simulation procedures to simultaneously compute an ergodic distribution of state variables, its support and the associated … decision rules. We differ from existing methods, however, in how we use simulation data to approximate decision rules. Instead …

effects. This simulation study used real-world data to compare model performance across a range of important statistical … performance was reduced when policy effectiveness had incremental, rather than instantaneous, onset. These findings highlight the …

effects. This simulation study used real-world data to compare model performance across a range of important statistical … performance was reduced when policy effectiveness had incremental, rather than instantaneous, onset. These findings highlight the …

This paper uses simulations to explore the properties of the HP filter of Hodrick and Prescott (1997), the BK filter of …