Solving the Stochastic Growth Model by Linear-Quadratic Approximation and by Value-Function Iteration
描述了三种近似方法求解NBER非线性理性预期模型项目中的增长模型，包括基于非随机稳态的线性二次逼近和状态空间离散化后的值函数迭代。
This article describes three approximation methods I used to solve the growth model (Model 1) studied by the National Bureau of Economic Research's nonlinear rational-expectations-modeling group project, the results of which were summarized by Taylor and Uhlig (1990). The methods involve computing exact solutions to models that approximate Model 1 in different ways. The first two methods approximate Model 1 about its nonstochastic steady state. The third method works with a version of the model in which the state space has been discretized. A value function iteration method is used to solve that model.