In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques. We focus particularly on the US economy but the methodology can be applied also to other economies. Specifically US economy has suffered a severe recession from 2008 to 2010 which practically breaks out conventional econometrics model attempts. Deep learning has the advantage that it models all macro variables simultaneously taking into account all interdependencies among them and detecting non-linear patterns which cannot be easily addressed under a univariate modelling framework. Our empirical results indicate that the deep learning methods have a superior out-of-sample performance when compared to traditional econometric techniques such as Bayesian Model Averaging (BMA). Therefore our results provide a concise view of a more robust method for assessing sovereign risk which is a crucial component in investment and monetary decisions.
翻译:在这项研究中,我们提出了一种利用深层次学习技术对一个国家的宏观经济状况进行即时预测和预测的新办法。我们特别侧重于美国经济,但这一方法也可以适用于其他经济体。具体地说,美国经济在2008年至2010年经历了严重的衰退,这实际上打破了传统的计量经济学模型尝试。深层次学习的好处是,它能同时模拟所有宏观变量,同时考虑它们之间的相互依存关系,并发现非线性模式,而这些非线性模式在单向建模框架下是不容易解决的。 我们的经验结果表明,深层次的学习方法与传统的计量生态技术(如巴伊西亚模型(BMA ) 相比,其表现优于标本。 因此,我们的成果为评估主权风险提供了一种更强有力的方法的简明观点,而主权风险是投资和货币决策的重要组成部分。