We develop numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. First, instead of standard least‐squares approximation methods, we examine a variety of alternatives, including least‐squares methods using singular value decomposition and Tikhonov regularization, least‐absolute deviations methods, and principal component regression method, all of which are numerically stable and can handle ill‐conditioned problems. Second, instead of conventional Monte Carlo integration, we use accurate quadrature and monomial integration. We test our generalized stochastic simulation algorithm (GSSA) in three applications: the standard representative–agent neoclassical growth model, a model with rare disasters, and a multicountry model with hundreds of state variables. GSSA is simple to program, and MATLAB codes are provided.

, Ivie, MECD and FEDER funds under the projects SEJ-2007-62656 and ECO2012-36719. Rafael Valero acknowledges support from MECD under the FPU program. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.