Local identification in DSGE models
Local identification in DSGE models
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DOI：
10.1016/j.jmoneco.2009.12.007
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发表时间：
2010-03
影响因子：
4.1
通讯作者：
Nikolay Iskrev
中科院分区：
文献类型：
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作者：
Nikolay Iskrev
识别的问题出现时，结构模型估计。缺乏识别意味着某些模型参数的经验含义要么无法检测，要么无法与其他参数的含义区分开来。因此，在估计之前必须验证可识别性。本文提供了一个简单的方法进行局部识别分析的线性DSGE模型，估计在充分和有限的信息设置。除了确定哪些参数是局部识别的，哪些不是，研究人员还可以确定识别失败是否是由于数据限制，例如缺乏对某些变量的观察，或者它们是否是模型结构所固有的。使用一个中等规模的DSGE模型的方法进行说明。
The issue of identification arises whenever structural models are estimated. Lack of identification means that the empirical implications of some model parameters are either undetectable or indistinguishable from the implications of other parameters. Therefore, identifiability must be verified prior to estimation. This paper provides a simple method for conducting local identification analysis in linearized DSGE models, estimated in both full and limited information settings. In addition to establishing which parameters are locally identified and which are not, researchers can determine whether the identification failures are due to data limitations, such as lack of observations for some variables, or whether they are intrinsic to the structure of the model. The methodology is illustrated using a medium-scale DSGE model.