Working Paper: NBER ID: w22402
Authors: Patrick Higgins; Tao Zha; Karen Zhong
Abstract: Although macroeconomic forecasting forms an integral part of the policymaking process, there has been a serious lack of rigorous and systematic research in the evaluation of out-of-sample model-based forecasts of China's real GDP growth and CPI inflation. This paper fills this research gap by providing a replicable forecasting model that beats a host of other competing models when measured by root mean square errors, especially over long-run forecast horizons. The model is shown to be capable of predicting turning points and to be usable for policy analysis under different scenarios. It predicts that China's future GDP growth will be of L-shape rather than U-shape.
Keywords: Macroeconomic forecasting; China; GDP growth; CPI inflation; Bayesian vector autoregression
JEL Codes: C53; E1; E17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| M2 growth (O42) | GDP growth (O49) |