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首页> 外文期刊>Journal of Economic Dynamics and Control >A new algorithm for solving dynamic stochastic macroeconomic models
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A new algorithm for solving dynamic stochastic macroeconomic models

机译：一种求解动态随机宏观经济模型的新算法

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This paper introduces a new algorithm, the recursive upwind Gauss-Seidel method, and applies it to solve a standard stochastic growth model in which the technology shocks exhibit heteroskedasticity. This method exploits the fact that the equations defining equilibrium can be viewed as a set of algebraic equations in the neighborhood of the steady-state. In a non-stochastic setting, the algorithm, in essence, continually extends a local solution to a globally accurate solution. When stochastic elements are introduced, it then uses a recursive scheme in order to determine the global solution. This method is compared to projection, perturbation, and linearization approaches and is shown to be fast and globally accurate. We also demonstrate that linearization methods perform poorly in an environment of heteroskedasticity even though the unconditional variance of technology shocks is relatively small and similar to that typically used in RBC analysis.
机译：本文介绍了一种新的算法，即递归逆风高斯-塞德尔方法，并将其应用于求解标准随机增长模型，该模型中技术冲击表现出异方差性。该方法利用了以下事实：定义平衡的方程可以看作是稳态附近的一组代数方程。在非随机设置中，该算法本质上将连续地将局部解扩展为全局精确解。当引入随机元素时，它随后使用递归方案来确定全局解决方案。将该方法与投影，摄动和线性化方法进行了比较，结果表明该方法是快速且全局准确的。我们还证明，即使技术冲击的无条件方差相对较小，并且与RBC分析中通常使用的方差相似，线性化方法在异方差性环境中的效果也很差。

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