Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout
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用深度学习生成美国 GDP 增长密度即时预测
- 研究问题
- 神经网络的 GDP 即时预测如何从点预测扩展为带有不确定性的预测分布，并与常数增长模型及动态因子模型比较预测表现？
- 方法与思路
- 作者以一维卷积神经网络为基础，采用 Bayes by Backprop 与 Monte Carlo dropout 生成经验预测分布，以其均值作为点即时预测。
- 材料陈述的贡献
- 摘要称，两种算法在 2012 年第一季度至 2022 年第四季度的点预测中优于所列基准，并能动态调整预测分布的均值、方差和偏度。
- 阅读关注点
- 应核对实时数据和发布滞后处理、网络训练与调参方式、预测分布生成步骤，以及密度评分、区间覆盖和基准比较是否充分。
- 阅读边界
- 依据所给 arXiv 预印本摘要，不能确认同行评审或期刊接收。摘要未报告密度校准数值；所述点预测优势不能直接证明预测分布更准确。
原文摘要
Recent results in the literature indicate that artificial neural networks (ANNs) can outperform the dynamic factor model (DFM) in terms of the accuracy of GDP nowcasts. Compared to the DFM, the performance advantage of these highly flexible, nonlinear estimators is particularly evident in periods of recessions and structural breaks. From the perspective of policy-makers, however, nowcasts are the most useful when they are conveyed with uncertainty attached to them. While the DFM and other classical time series approaches analytically derive the predictive (conditional) distribution for GDP growth, ANNs can only produce point nowcasts based on their default training procedure (backpropagation). To fill this gap, first in the literature, we adapt two different deep learning algorithms that enable ANNs to generate density nowcasts for U.S. GDP growth: Bayes by Backprop and Monte Carlo dropout. The accuracy of point nowcasts, defined as the mean of the empirical predictive distribution, is evaluated relative to a naive constant growth model for GDP and a benchmark DFM specification. Using a 1D CNN as the underlying ANN architecture, both algorithms outperform those benchmarks during the evaluation period (2012:Q1 -- 2022:Q4). Furthermore, both algorithms are able to dynamically adjust the location (mean), scale (variance), and shape (skew) of the empirical predictive distribution. The results indicate that both Bayes by Backprop and Monte Carlo dropout can effectively augment the scope and functionality of ANNs, rendering them a fully compatible and competitive alternative for classical time series approaches.