Deep Learning Macroeconomics
深度学习宏观经济学AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确涉及宏观经济周期、产出缺口，以及不同频率时间序列的映射和外推，对应时间序列与预测方向。
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深度学习宏观经济学
- 研究问题
- 面对宏观数据有限和变量关系非线性的困难，如何利用相关领域的信息识别经济周期、估计产出缺口并处理不同频率的数据？
- 方法与思路
- 作者提出以深度学习实施跨领域迁移学习，并利用多层非线性表示，将高频变量映射到低频变量，开展周期分类和产出缺口估计。
- 材料陈述的贡献
- 摘要称，美国数据训练的模型能够识别巴西和欧洲的样本外经济周期，并支持产出缺口估计及时间序列插值、分配与外推。
- 阅读关注点
- 阅读时应核对跨国迁移的训练与验证划分、周期和产出缺口的评价基准，以及不同频率映射是否使用了预测时不可获得的信息。
- 阅读边界
- 所用版本为 arXiv 预印本，同行评审及期刊接收未核验；摘要未说明样本规模、误差指标和比较基准，无法判断表现的稳健性。
原文摘要
Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. We explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate a economic-based output gap. Next, since deep learning methods are a way of learning representations, those that are formed by the composition of multiple non-linear transformations, to yield more abstract representations, we apply deep learning for mapping low-frequency from high-frequency variables. The results obtained show the suitability of deep learning models applied to macroeconomic problems. First, models learned to classify United States business cycles correctly. Then, applying transfer learning, they were able to identify the business cycles of out-of-sample Brazilian and European data. Along the same lines, the models learned to estimate the output gap based on the U.S. data and obtained good performance when faced with Brazilian data. Additionally, deep learning proved adequate for mapping low-frequency variables from high-frequency data to interpolate, distribute, and extrapolate time series by related series.