论文标题
具有替代大数据的宏观经济分析的知识图
The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data
论文作者
论文摘要
当前的宏观经济学知识系统建立在少数变量之间的相互作用上，因为传统的宏观经济模型大多可以处理少数输入。使用大数据的最新工作表明，大量变量积极地推动了总经济的动态。在本文中，我们介绍了一个知识图（kg），该图不仅包括传统经济变量之间的联系，还包括新的替代大数据变量。我们通过在学术文献和研究报告的大量文本数据上应用高级自然语言处理（NLP）工具来提取这些新变量和链接。作为潜在应用的一个例子，我们将其用作先验知识，以选择宏观经济学中经济预测模型的变量。与统计变量选择方法相比，基于KG的方法的预测准确性明显更高，尤其是对于长期预测。
The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.
