Abstract:
We develop models for examining possible predictors of growth of China’s foreign exchange reserves
that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing
with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model
selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1)
models, recursive OLS with all predictive variables models) but also the Bayesian model averaging
(BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall
across all forecast horizons. While some predictors matter more than others over the forecast horizons,
there are few that stand the test of time. The US-China interest rate differential has a superior predictive
power among the 13 predictors considered, followed by the nominal effective exchange rate and the
interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper
prices alternates, depending on the commodity cycles. Policy implications are also provided.
