Does the Quantile Regression Forest Learn More Information on Chinese Systemic Risk?
30 Pages Posted: 15 Jan 2020
Date Written: December 26, 2019
This article applies the quantile regression forest (QRF), which is an improved method for predicting future macroeconomic shocks. We summarize 31 different indexes of Chinese systemic risk and construct predictors using principal component analysis to predict Chinese macroeconomic downside risk. The two forecasting methods, the multiple quantile regression forest (MQRF) and the principal component quantile regression forest (PCQRF), both outperform the quantile regression (QR), the multiple quantile regression (MQR) and the principal component quantile regression (PCQR). Furthermore, we find that with increased systemic risk indexes, more information about the left tail of macroeconomic shocks could be identified by the QRF.
Keywords: Quantile Regression Forest, Systemic Risk, Macroeconomic Forecast
JEL Classification: G10, E37, C53, C55
Suggested Citation: Suggested Citation