Comment

Steinar Holden
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Abstract

There is now a fast growing literature extending real business cycle (RBC) models to include search and matching frictions. The aim of the literature is to explore the business cycle properties of the labor market as to what the basic mechanisms are and which shocks that matter. This is a very interesting area of research, and the literature has made considerable progress over the last fi ve to seven years. The paper of Justiniano and Michelacci makes a valuable contribution to this literature along several lines. First, it develops a more elaborate model than the existing ones, including novel shocks and mechanisms. Second, it uses a rather advanced estimation method, with a full information Bayesian method. Third, it includes a broader empirical application, analyzing the empirical performance of the United States and fi ve European countries (France, Germany, Norway, Sweden, and the United Kingdom). In this discussion, I will fi rst briefl y describe the key elements of the model, then consider some of them in more detail. Finally, I will discuss what we can learn from this paper, as well as from the literature to which it belongs. As will be clear, my overall view of the paper and the literature in general is favorable. Yet I also think that the analysis gives a somewhat exaggerated picture of how much of the variation in the data that the model is really able to explain. Future research should explore the validity of the key shocks and mechanisms, also by use of other methods and other type of data.
评论
现在有一个快速增长的文献扩展真实商业周期(RBC)模型,包括搜索和匹配摩擦。这些文献的目的是探索劳动力市场的商业周期属性,即基本机制是什么以及哪些冲击是重要的。这是一个非常有趣的研究领域,在过去的五到七年里,相关文献取得了相当大的进展。查士丁尼亚诺和米凯莱奇的论文在几个方面对这一文献做出了有价值的贡献。首先,它开发了一个比现有模型更复杂的模型,包括新的冲击和机制。其次,它采用了一种较为先进的估计方法,即全信息贝叶斯方法。第三,它包含了更广泛的实证应用,分析了美国和五个欧洲国家(法国、德国、挪威、瑞典和英国)的实证表现。在本讨论中,我将首先简要描述模型的关键元素,然后更详细地考虑其中的一些元素。最后,我将讨论我们可以从这篇论文以及它所属的文献中学到什么。很明显,我对这篇论文和文献的总体看法是有利的。然而,我也认为,对于模型真正能够解释的数据变化有多少,分析给出了一幅有些夸张的画面。未来的研究应该探索关键冲击和机制的有效性,也可以通过使用其他方法和其他类型的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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