Solving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality
提出一个基于稀疏网格的贝叶斯估计框架，用于克服非线性动态经济模型近似中的维度诅咒，包括全局多项式近似、积分求积和三种新非线性状态空间滤波器，并开发了开源软件JBendge。
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 the model solution, to the quadrature of integrals arising as rational expectations, and to three new nonlinear state space filters which speed up the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a 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. Copyright 2010 The Econometric Society.