论文标题
深度动态因子模型
Deep Dynamic Factor Models
论文作者
论文摘要
一个新颖的深度神经网络框架（我们称为深度动态因素模型（D $^2 $ fm））能够将可用信息从数百个宏观经济和财务时间序列中编码为少数未观察到的潜在状态。尽管精神与传统动态因素模型（DFM）相似，但这种新模型允许由于自动编码器神经网络结构而引起的因素和可观察结果之间的非线性。但是，根据设计，模型的潜在状态仍然可以解释为标准因素模型。无论是在完全实时的样本外现象还是通过美国数据进行预测练习，以及在蒙特卡洛实验中，D $^2 $ fm都改善了最先进的DFM的性能。
A novel deep neural network framework -- that we refer to as Deep Dynamic Factor Model (D$^2$FM) --, is able to encode the information available, from hundreds of macroeconomic and financial time-series into a handful of unobserved latent states. While similar in spirit to traditional dynamic factor models (DFMs), differently from those, this new class of models allows for nonlinearities between factors and observables due to the autoencoder neural network structure. However, by design, the latent states of the model can still be interpreted as in a standard factor model. Both in a fully real-time out-of-sample nowcasting and forecasting exercise with US data and in a Monte Carlo experiment, the D$^2$FM improves over the performances of a state-of-the-art DFM.
