Modeling inflation expectations in forward-looking interest rate and money growth rules

IF 1.9 3区 经济学 Q2 ECONOMICS
Zhengyang Chen , Victor J. Valcarcel
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引用次数: 0

Abstract

We propose a novel approach that directly embeds rational expectations (RE) into a low-dimensional structural vector autoregression (SVAR) without the need for any mapping to a dynamic stochastic general equilibrium (DSGE) model. Beginning from a fully specified “consensus” structural model, we establish an instrumental variable procedure internal to the SVAR to obtain RE-consistent structural responses to identified monetary policy shocks. Our RE-SVAR framework facilitates a comparison across two alternative monetary policy indicators that accommodate long horizons in the formation of inflation expectations in the policy rule. We construct clouds of responses of inflation and economic activity to monetary policy shocks. We find large regions of puzzling responses to innovations in the federal funds rate. This suggests that indicator often requires being augmented with more information in standard VAR settings. A money growth rule characterization—with Divisia M4 as a policy indicator—exhibits comparatively larger regions of sensible responses within a low-dimensional textbook model of the economy.
在前瞻性利率和货币增长规则中建模通胀预期
我们提出了一种新的方法,将理性期望(RE)直接嵌入到低维结构向量自回归(SVAR)中,而无需映射到动态随机一般均衡(DSGE)模型。从一个完全指定的“共识”结构模型开始,我们在SVAR内部建立了一个工具变量程序,以获得对确定的货币政策冲击的re -一致的结构响应。我们的RE-SVAR框架促进了两种替代货币政策指标之间的比较,这些指标在政策规则中适应了通胀预期形成的长期视野。我们构建了通货膨胀和经济活动对货币政策冲击的反应云。我们发现,很多地区对联邦基金利率的创新反应令人困惑。这表明,在标准VAR设置中,该指标通常需要添加更多信息。货币增长规则的特征——以division M4作为政策指标——在低维度的教科书经济模型中显示出相对较大的明智反应区域。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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