Does the Quantile Regression Forest Learn More Information on Chinese Systemic Risk?
SSRN, ISSN: 1556-5068
2020
- 660Usage
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This article applies the quantile regression forest (QRF), which is an improved method for predicting future monetary policy and macroeconomic downside risks in China. The information used to forecast is derived from Chinese systemic risk. We construct two Chinese systemic risk information sets, one is the old information set with 12 indexes, the other is our information set with 19 indexes added. We also applied two methods to learn systemic risk information, including multiple regression and principal component analysis (PCA). We show that the multiple quantile regression forest (MQRF) and the principal component quantile regression forest (PCQRF) exhibit a superior out-of-sample forecasting ability when compared to alternative forecasting models, such as the multiple quantile regression (MQR) and the principal component quantile regression (PCQR). Furthermore, our systemic risk information set has good economic implications in predicting China’s monetary policy and macroeconomic downside risks.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85109987013&origin=inward; http://dx.doi.org/10.2139/ssrn.3556400; https://www.ssrn.com/abstract=3556400; https://dx.doi.org/10.2139/ssrn.3556400; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3556400; https://ssrn.com/abstract=3556400
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know