论文标题
国家通货膨胀的主要驱动力
Dominant Drivers of National Inflation
论文作者
论文摘要
对于西方经济体而言，长期以来的现象即将到来：通货膨胀率上升。我们提出了一种新颖的方法，将D2ML命名为识别国家通货膨胀的驱动力。 D2ML将模型选择的机器学习与时间相关数据和图形模型结合在一起，以估计协方差矩阵的倒数，然后将其用于识别主要驱动因素。使用33个国家 /地区的数据集，我们发现美国通货膨胀率和石油价格是全国通货膨胀率的主要驱动因素。对于更一般的框架，我们进行了蒙特卡洛模拟，以表明我们的估计器正确识别了主要驱动因素。
For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation. D2ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to identify dominant drivers. Using a dataset of 33 countries, we find that the US inflation rate and oil prices are dominant drivers of national inflation rates. For a more general framework, we carry out Monte Carlo simulations to show that our estimator correctly identifies dominant drivers.
