美国经济增长与波动关系的计量经济学历史:1919-2017 年

IF 1.5 1区 历史学 Q2 ECONOMICS Cliometrica Pub Date : 2020-05-28 DOI:10.1007/s11698-020-00209-y
Amélie Charles, Olivier Darné
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引用次数: 0

摘要

在本文中,我们使用标准 GARCH-M 框架和 1919 年 1 月至 2017 年 12 月期间的美国月度工业生产指数(IPI),通过考虑冲击和方差变化的存在,研究了产出波动与增长之间的关系。结果表明,IPI 增长受到巨大冲击的强烈影响,这些冲击与某些行业的罢工、经济衰退、第二次世界大战和自然灾害有关。我们还确定了几个具有不同波动水平的子时期,其波动性随着子时期的变化而下降,其中二战前时期(1919-1946 年)是波动性最高的时期,而全球金融危机后时期(2010-2017 年)是波动性最低的时期。我们发现,在 1919-2017 年的整个样本期间以及所有子期间,都没有证据表明产出波动性与其增长之间存在关系。从宏观经济的角度来看,这意味着以 IPI 增长率衡量的经济表现并不取决于以 IPI 波动率衡量的不确定性。
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Econometric history of the growth–volatility relationship in the USA: 1919–2017

In this paper, we investigate the relationship between output volatility and growth using the standard GARCH-M framework and the US monthly industrial production index (IPI) for the period January 1919–December 2017, by taking into account the presence of shocks and variance changes. The results show that the IPI growth is strongly affected by large shocks which are associated with strikes in some industries, recessions, World War II and natural disasters. We also identify several subperiods with different level of volatility where the volatility declines along the subperiods, with the pre-WWII period (1919–1946) the highest volatile period and the aftermath period of the GFC (2010–2017) the lowest volatile period. We find no evidence of relationship between output volatility and its growth during the full sample 1919–2017 and also for all the subperiods. From a macroeconomic point of view, this implies that economic performances, as measured by IPI growth, do not depend on the uncertainty as measured by IPI volatility.

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来源期刊
Cliometrica
Cliometrica Multiple-
CiteScore
2.70
自引率
18.80%
发文量
16
期刊介绍: Cliometrica provides a leading forum for exchange of ideas and research in all facets, in all historical periods and in all geographical locations of historical economics. The journal encourages the methodological debate, the use of economic theory in general and model building in particular, the reliance upon quantification to buttress the models with historical data, the use of the more standard historical knowledge to broaden the understanding and suggesting new avenues of research, and the use of statistical theory and econometrics to combine models with data in a single consistent explanation. The highest standards of quality are promoted. All articles will be subject to Cliometrica''s peer review process. On occasion, specialised topics may be presented in a special issue. Officially cited as: Cliometrica
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