000K  utf8
0100  1806098725
1100  2021$c2021-06-10
1500  eng
2051  10.1111/eufm.12325
3000  Basse, Tobias
3010  Gonzalez, Miguel Rodriguez
3010  Kunze, Frederik
3010  Saft, Danilo
4000  Leading indicators for US house prices: new evidence and implications for EU financial risk managers  [Basse, Tobias]
4060  22 Seiten
4209  This study draws on machine learning as a means to causal inference for econometric investigation. We utilize the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case-Shiller 20 City Composite Home Price Index (SPCS20). The empirical evidence implies that the survey data can help to predict US house prices. This finding extends the results of Granger causality tests performed by Rodriguez Gonzalez et al. in 2018 using a new machine learning approach that methodologically differs from traditional methods in empirical financial research.
4950  https://doi.org/10.1111/eufm.12325$xR$3Volltext$534
4961  http://uri.gbv.de/document/gvk:ppn:1806098725
5051  330
5550  financial risk management
5550  leading indicators
5550  machine learning
5550  transfer entropy
5550  US house prices