你匹配什么很重要:数据对DSGE估计的影响

Pablo A. Guerrón-Quintana
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引用次数: 84

摘要

本文探讨了在动态随机一般均衡(DSGE)模型的估计中使用可观测值的替代组合的效果。我发现,描述价格和工资中的泰勒规则和粘性契约的结构参数的估计对一组可观测值特别敏感。就模型的预测而言,排除一些可观测值可能导致估计参数具有意想不到的结果,例如积极的技术冲击之后的衰退。更重要的是,提出了两种评估不同观测集的方法。这些措施有利于由七个可观测值组成的数据集。版权所有©2009 John Wiley & Sons, Ltd
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What You Match Does Matter: The Effects of Data on DSGE Estimation
This paper explores the effects of using alternative combinations of observables for the estimation of Dynamic Stochastic General Equilibrium (DSGE) models. I find that the estimation of structural parameters describing the Taylor rule and sticky contracts in prices and wages is particularly sensitive to the set of observables. In terms of the model's predictions, the exclusion of some observables may lead to estimated parameters with unexpected outcomes, such as recessions following a positive technology shock. More importantly, two ways to assess different sets of observables are proposed. These measures favor a dataset consisting of seven observables. Copyright © 2009 John Wiley & Sons, Ltd.
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