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来源类型 | Discussion paper |
规范类型 | 论文 |
来源ID | DP7813 |
DP7813 Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting in U.S. Data | |
Juan Francisco Rubio-Ramírez; Jesus Fernandez-Villaverde; Pablo A. Guerron-Quintana | |
发表日期 | 2010-05-23 |
出版年 | 2010 |
语种 | 英语 |
摘要 | This paper compares the role of stochastic volatility versus changes in monetary policy rules in accounting for the time-varying volatility of U.S. aggregate data. Of special interest to us is understanding the sources of the great moderation of business cycle fluctuations that the U.S. economy experienced between 1984 and 2007. To explore this issue, we build a medium-scale dynamic stochastic general equilibrium (DSGE) model with both stochastic volatility and parameter drifting in the Taylor rule and we estimate it non-linearly using U.S. data and Bayesian methods. Methodologically, we show how to confront such a rich model with the data by exploiting the structure of the high-order approximation to the decision rules that characterize the equilibrium of the economy. Our main empirical findings are: 1) even after controlling for stochastic volatility (and there is a fair amount of it), there is overwhelming evidence of changes in monetary policy during the analyzed period; 2) however, these changes in monetary policy mattered little for the great moderation; 3) most of the great performance of the U.S. economy during the 1990s was a result of good shocks; and 4) the response of monetary policy to inflation under Burns, Miller, and Greenspan was similar, while it was much higher under Volcker. |
主题 | International Macroeconomics |
关键词 | Bayesian methods Dsge models Parameter drifting stochastic volatility |
URL | https://cepr.org/publications/dp7813 |
来源智库 | Centre for Economic Policy Research (United Kingdom) |
资源类型 | 智库出版物 |
条目标识符 | http://119.78.100.153/handle/2XGU8XDN/536650 |
推荐引用方式 GB/T 7714 | Juan Francisco Rubio-Ramírez,Jesus Fernandez-Villaverde,Pablo A. Guerron-Quintana. DP7813 Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting in U.S. Data. 2010. |
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