Leading indicators for US house prices: New evidence and implications for EU financial risk managers
利用机器学习中的转移熵方法，发现美国全国住宅建筑商协会指数能预测房价，为欧盟金融风险管理者提供了新证据。
Abstract This study draws on machine learning as a means to causal inference for econometric investigation. We utilize the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case‐Shiller 20 City Composite Home Price Index (SPCS20). The empirical evidence implies that the survey data can help to predict US house prices. This finding extends the results of Granger causality tests performed by Rodriguez Gonzalez et al. in 2018 using a new machine learning approach that methodologically differs from traditional methods in empirical financial research.