Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction
Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction
复制标题
美国通胀中未观察到的随机波动成分：估计和信号提取
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. J. Koopman
中科院分区：
文献类型：
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作者：
Mengheng Li;S. J. Koopman
我们认为不可观测的组件时间序列模型的组件是随机随时间推移而变化，并受到随机波动。它使解纠缠的动态结构的平均值和方差的观察到的时间序列。我们开发了一个模拟的最大似然估计方法的基础上的重要性抽样，并评估其性能在蒙特卡洛研究。这个模型框架的趋势，季节性和不规则的组件被应用到季度和月度美国通货膨胀的实证研究。我们发现，季度通胀的持续性在2008年金融危机期间有所增加，而最近又恢复到危机前的水平。提取的趋势成分的波动模式可以与20世纪70年代的能源冲击，而不规则的组成部分，从20世纪80年代的货币制度变化的响应。1990年代初，季节性部分的变化幅度最大。最后，我们提出了经验证据的点和密度预测的准确性，每月美国通货膨胀的相对改善。
We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated maximum likelihood estimation method based on importance sampling and assess its performance in a Monte Carlo study. This modelling framework with trend, seasonal and irregular components is applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility pattern for the trend component can be associated with the energy shocks in the 1970s while that for the irregular component responds to the monetary regime changes from the 1980s. The scale of the changes in the seasonal component has been largest during the beginning of the 1990s. We finally present empirical evidence of relative improvements in the accuracies of point and density forecasts for monthly US inflation.