SOLVING, ESTIMATING, AND SELECTING NONLINEAR DYNAMIC MODELS WITHOUT THE CURSE OF DIMENSIONALITY

摘要

We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids to overcome the curse of dimensionality for approximations. We apply sparse grids to a global polynomial approximation of themodel solution, to the quadrature of integrals arising as rational expectations, and to three new nonlinear state space niters which speed up the sequential importance resampling particle filter. The posterior of the structural parameters is estimated bya new Metropolis-Hastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the parameterization for an appropriate acceptance ratio, and allows a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge for the solution and estimation of a general class of models.