Parameterized Expectations Algorithm and the Moving Bounds
针对参数化期望算法（PEA）在求解非线性随机动态模型时可能不收敛的问题，提出一种通过人工限制模拟序列边界并逐步移除的简单改进，使算法能从非随机稳态出发系统收敛到平稳解。
The Parameterized Expectations Algorithm (PEA) is a powerful tool for solving nonlinear stochastic dynamic models. However, it has an important shortcoming: it is not a contraction mapping technique and thus does not guarantee a solution will be found. We suggest a simple modification that enhances the convergence property of the algorithm. The idea is to rule out the possibility of (ex)implosive behavior by artificially restricting the simulated series within certain bounds. As the solution is refined along the iterations, the bounds are gradually removed. The modified PEA can systematically converge to the stationary solution starting from the nonstochastic steady state.