Наукастинг темпов роста стоимостных объемов экспорта и импорта по товарным группам

Nowcasting the growth rates of the export and import by commodity groups

Ksenia Maiorova and Nikita Fokin

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we consider a set of machine learning and econometrics models, namely: Elastic Net, Random Forest, XGBoost and SSVS as applied to nowcasting a large dataset of USD volumes of Russian exports and imports by commodity group. We use lags of the volumes of export and import commodity groups, prices for some imported and exported goods and other variables, due to which the curse of dimensionality becomes quite acute. The models we use are very popular and 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 model is the weighted model of machine learning methods, which outperforms the ARIMA benchmark model in nowcasting the volume of both exports and imports. In the case of the largest commodities groups, we often get a significantly more accurate nowcasts then ARIMA model, according to the Diebold-Mariano test. In addition, nowcasts turns out to be quite close to the historical forecasts of the Bank of Russia, being constructed in similar conditions.

Keywords: наукастинг; внешняя торговля; проклятие размерности; машинное обучение; российская экономика (search for similar items in EconPapers)
JEL-codes: C52 C53 C55 F17 (search for similar items in EconPapers)
Date: 2020-06
New Economics Papers: this item is included in nep-big, nep-cis, nep-cmp, nep-for, nep-isf and nep-ore
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