Nowcasting Growth Rates of Russia’s Export and Import by Commodity Group
Ksenia Mayorova () and
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Ksenia Mayorova: RANEPA
Russian Journal of Money and Finance, 2021, vol. 80, issue 3, 34–48
In this paper, we apply a set of machine learning and econometrics models, namely: Elastic Net, Random Forest, XGBoost, and SSVS to nowcast (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.
Keywords: nowcasting; foreign trade; curse of dimensionality; machine learning; Russian economy (search for similar items in EconPapers)
JEL-codes: C52 C53 C55 F17 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:80:y:2019:i:3:p:34-48
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