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A system reduction method to efficiently solve DSGE models

机译:一种有效简化DSGE模型的系统简化方法

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摘要

The paper presents a system reduction method (SRM) to improve the computational time to solve a large class of dynamic stochastic general equilibrium (DSCE) models with the methods of Anderson and Moore (1985), Klein (2000), Sims (2002) or Uhlig (1995). 1 measure the efficiency gains with seven models ranging from 47 to 333 equations. The time reduction for the Anderson-Moore algorithm aim ranges from 10% to 71%; Klein's function solab reduces its time between 51% and 79%; the time reduction for Sims' function gensys increases from 25% to 59%; Uhlig's function solve reduces its time between 31% and 87%. The time reduction can be crucial for Bayesian estimation of medium to large scale models.
机译:本文提出了一种系统简化方法(SRM),以缩短计算时间,以解决安德森(Anderson)和摩尔(Moore)(1985),克莱因(2000),西姆斯(2002)或Uhlig(1995)。 1用从47到333等式的七个模型测量效率增益。 Anderson-Moore算法目标的时间减少范围从10%到71%; Klein的功能解决方案将其时间减少了51%至79%; Sims函数gensys的时间减少从25%增加到59%; Uhlig的功能解决方案将其时间减少了31%至87%。对于中型到大型模型的贝叶斯估计,时间减少可能至关重要。

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