Replicating business cycles and asset returns with sentiment and low risk aversion

IF 1.9 3区 经济学 Q2 ECONOMICS
Kevin J. Lansing
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引用次数: 0

Abstract

I solve for the sequences of shocks (or wedges) that allow a standard real business cycle model to exactly replicate the quarterly time paths of U.S. macroeconomic variables and asset returns since 1960. The resulting shock sequences can be grouped into three main categories: (1) shocks that affect household sentiment and preferences, (2) shocks that appear in the law of motion for capital, and (3) shocks that appear in the production function for output. For most variables including output, no single shock category is clearly dominant in explaining the observed movements in U.S. data. While some variables are driven by a single dominant shock category, the dominant category is different for each of those variables. The results imply that there is no “most important shock.” Rather, U.S. economic outcomes have been shaped by a complex and time-varying mixture of fundamental and non-fundamental disturbances.

用情绪和低风险规避复制商业周期和资产回报
我求解了冲击序列(或楔形),使标准实际商业周期模型能够精确复制自 1960 年以来美国宏观经济变量和资产回报的季度时间路径。由此得出的冲击序列可分为三大类:(1)影响家庭情绪和偏好的冲击,(2)出现在资本运动规律中的冲击,以及(3)出现在产出生产函数中的冲击。对于包括产出在内的大多数变量而言,没有哪一类冲击能明显地解释美国数据中观察到的变动。虽然有些变量是由单一主导冲击类别驱动的,但每个变量的主导冲击类别是不同的。这些结果表明,不存在 "最重要的冲击"。相反,美国的经济结果是由复杂的、随时间变化的基本面和非基本面扰动混合形成的。
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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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