Measuring monetary policy with empirically grounded identifying restrictions

被引:0
|
作者
Piyachart Phiromswad
机构
[1] Sasin Graduate Institute of Business Administration of Chulalongkorn University,
来源
Empirical Economics | 2014年 / 46卷
关键词
Monetary policy; Monetary policy shock; Graph theory; Causality; Causal search; PC algorithm; CPC algorithm; SVAR; Recursiveness assumption; C30; C32; C51;
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学科分类号
摘要
This article reevaluates the impulse response functions (IRFs) to a monetary policy shock of the structural vector autoregression (SVAR). Identifying restrictions are specified and justified based on empirical evidence,i.e., conditional independence relations of variables, which is an important dimension that a good model must be able to mimic. The empirical-based approach is able to significant narrow down the set of admissible causal orders to identify the IRFs to a monetary policy shock (from 2,482 to 8). I find that most of the qualitative “stylized” features reported in the literature remain intact. However, the quantitative predictions are much less certain than what is commonly perceived.
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页码:681 / 699
页数:18
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