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 modelbased 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.
out of sample policy projections scenario analysis probability bands density forecasts random walk Bayesian priors