Mixed Frequency Structural Models: Identification, Estimation, and Policy Analysis
Mixed Frequency Structural Models: Identification, Estimation, and Policy Analysis
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混合频率结构模型：识别、估计和政策分析
DOI：
10.2139/ssrn.2352986
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Massimiliano Marcellino
中科院分区：
文献类型：
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作者：
Claudia Foroni;Massimiliano Marcellino
在本文中，我们分析表明，与模拟实验和实际数据的时间尺度的DSGE模型和用于其估计的时间序列数据之间的不匹配通常会产生识别问题，引入估计偏差和扭曲的政策分析的结果。在建设性的一面，我们证明了使用混合频率数据，结合适当的估计方法，可以减轻时间聚集偏差，减轻识别问题，并产生更可靠的政策结论。这些问题和可能的补救措施是在标准的结构性货币政策模型的背景下说明的。
In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identification problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identification issues, and yield more reliable policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.