Maiorova, Ksenia and Fokin, Nikita (2020): Наукастинг темпов роста стоимостных объемов экспорта и импорта по товарным группам.

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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 DieboldMariano test. In addition, nowcasts turns out to be quite close to the historical forecasts of the Bank of Russia, being constructed in similar conditions.
Item Type:  MPRA Paper 

Original Title:  Наукастинг темпов роста стоимостных объемов экспорта и импорта по товарным группам 
English Title:  Nowcasting the growth rates of the export and import by commodity groups 
Language:  Russian 
Keywords:  наукастинг; внешняя торговля; проклятие размерности; машинное обучение; российская экономика 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C55  Large Data Sets: Modeling and Analysis F  International Economics > F1  Trade > F17  Trade Forecasting and Simulation 
Item ID:  109557 
Depositing User:  Nikita Fokin 
Date Deposited:  14 Sep 2021 20:51 
Last Modified:  14 Sep 2021 20:51 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/109557 