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Assessing Structural VARs

机译:评估结构VAR

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This paper analyzes the quality of VAR-based procedures for estimating the response of the economy to a shock. We focus on two key issues. First, do VAR-based confidence intervals accurately reflect the actual degree of sampling uncertainty associated with impulse response functions? Second, what is the size of bias relative to confidence intervals, and how do coverage rates of confidence intervals compare with their nominal size? We address these questions using data generated from a series of estimated dynamic, stochastic general equilibrium models. We organize most of our analysis around a particular question that has attracted a great deal of attention in the literature: How do hours worked respond to an identified shock? In all of our examples, as long as the variance in hours worked due to a given shock is above the remarkably low number of 1 percent, structural VARs perform well. This finding is true regardless of whether identification is based on short-run or long-run restrictions. Confidence intervals are wider in the case of long-run restrictions. Even so, long-run identified VARs can be useful for discriminating among competing economic models.
机译:本文分析了基于VAR的程序的质量,以评估经济对冲击的反应。我们专注于两个关键问题。首先,基于VAR的置信区间是否可以准确反映与脉冲响应函数相关的采样不确定性的实际程度?其次,相对于置信区间的偏差的大小是多少,置信区间的覆盖率与其标称大小相比如何?我们使用从一系列估计的动态,随机一般均衡模型生成的数据来解决这些问题。我们围绕一个特定的问题组织大多数分析,该问题在文献中引起了极大的关注:工作时间如何应对已确定的冲击?在我们所有的示例中,只要由于给定冲击而导致的工作时数方差高于1%的极低值,结构VAR就会表现良好。无论标识是基于短期还是长期限制,此发现都是正确的。在长期限制的情况下,置信区间更宽。即便如此,长期确定的增值经销商仍可用于区分竞争性经济模型。

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