Nowcasting Growth Rates of Russia’s Export and Import by Commodity Group

Abstract

In this paper, we apply a set of machine learning and econometrics models, namely: Elastic Net, Random Forest, XGBoost, and SSVS to nowcasting (estimate for the current period) the dollar volumes of Russian exports and imports by a commodity group. We use lags in the volumes of export and import commodity groups, and exchange prices for some goods and other variables, due to which the curse of dimensionality becomes quite acute. The models we use have proven themselves well in forecasting in the presence of the curse of dimensionality, when the number of model parameters exceeds the number of observations. The best-performing model appears to be the weighted machine learning model, which outperforms the ARIMA benchmark model in nowcasting the volume of both exports and imports. According to the Diebold– Mariano test, in the case of the largest commodity groups our model often manages to obtain significantly more accurate nowcasts relative to the ARIMA model. The resulting estimates turn out to be quite close to the Bank of Russia’s historical forecasts built under comparable conditions.