Patrick Higgins, Tao Zha, and Karen Zhong

Working Paper 2016-7
July 2016

Download the full text of this paper (407 KB) Adobe PDF file format

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 gross domestic product (GDP) growth and consumer price index 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 usable for policy analysis under different scenarios. It predicts that China's future GDP growth will be of an L-shape rather than a U-shape.

JEL classification: E10, E40, C53

Key words: out of sample, policy projections, scenario analysis, probability bands, density forecasts, random walk, Bayesian priors

The authors thank Chun Chang, Xiang Deng, and Zhao Li for helpful discussions. They are especially grateful to Yandong Jia at the People's Bank of China, who has generously offered insights into the Chinese data and the practice of China's monetary policy. This research is supported in part by the National Natural Science Foundation of China research grant nos. 71473168 and 71473169. The views expressed here are the authors' and not necessarily those of the Federal Reserve Bank of Atlanta, the Federal Reserve System, or the National Bureau of Economic Research. Any remaining errors are the authors' responsibility.
Please address questions regarding content to Patrick Higgins, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470,; Tao Zha, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470 and Emory University and also NBER,; or Karen Zhong, Shanghai Advanced Institute of Finance, Shanghai Jiaotong University, China,
Subscribe to receive e-mail notifications about new papers.