衡量适应性学习下非常规货币政策工具的效果

IF 1.9 3区 经济学 Q2 ECONOMICS
Stephen J. Cole , Sungjun Huh
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引用次数: 0

摘要

我们利用 Sims 等人(2023 年)的四方程新凯恩斯主义模型,比较了前瞻性指导和量化宽松政策的经济效应,其中代理人通过自适应学习规则形成预期。结果表明,与量化宽松政策相比,前瞻性指导对宏观经济变量的影响更大。自适应学习代理估计前瞻性指导对经济的影响更大,从而对预期产生更大影响,进而影响同期通货膨胀。然而,前瞻性指导与量化宽松政策之间的绩效差距会发生变化。如果量化宽松包括预期冲击,更多家庭通过长期借款为消费提供资金,中央银行在长期借款市场上提供更大比例的流动性,量化宽松的绩效就会提高,有时甚至会超过前瞻性指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring the effects of unconventional monetary policy tools under adaptive learning

We compare the economic effects of forward guidance and quantitative easing utilizing the four-equation New Keynesian model of Sims et al. (2023) with agents forming expectations via an adaptive learning rule. The results indicate forward guidance can have a greater influence on macroeconomic variables compared to quantitative easing. Adaptive learning agents estimate a higher effect of forward guidance on the economy leading to a greater impact on expectations, and thus, contemporaneous inflation. However, the performance gap between forward guidance and quantitative easing can change. If quantitative easing includes anticipated shocks, more households finance consumption through long-term borrowing, and the central bank provides a greater percentage of liquidity in the long-term borrowing market, the performance of quantitative easing can increase, and at times, outperform forward guidance.

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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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