Наукастинг темпов роста стоимостных объемов экспорта и импорта по товарным группам

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

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.

• Item Type: MPRA Paper
• 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

References

Baybuza I. (2018) Inflation forecasting using machine learning methods. Russian Journal of Money and Finance, 77(4), pp. 42-59.

Breiman L. (2001) Random forests. Machine learning, 45(1), pp. 5-32.

Chen T. G. (2016) XGB: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794.

Clarida R. H. (1996) Consumption, Import Prices, and the Demand for Imported Consumer Durables: A Structural Econometric Investigation. The Review of Economics and Statistics, 78(3), pp. 369-374.

Diebold, F. X., Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & economic statistics, 20(1), pp. 134-144.

Emelyanov S. S. (2007) Modeling of export and import of the Russian Federation in the system of forecasting and analytical calculations. Studies on Russian Economic Development, 2, pp. 116-126.

Gareev M. (2020) Use of Machine Learning Methods to Forecast Investment in Russia. Russian Journal of Money and Finance, 79(1), pp. 33-56.

George E. I., McCulloch R. E. (1993) Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88(423), pp. 881-889.

Jokubaitis S., Celov D., Leipus R. (2020) Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run. International Journal of Forecasting (forthcoming).

Koop G. M. (2013) Forecasting with medium and large Bayesian VARs. Journal of Applied Econometrics, 28(2), pp. 177-203.

Li, J., Chen, W. (2014) Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models. International Journal of Forecasting, 30(4), pp.996-1015.

Nicholson W. B., Matteson D. S., Bien J. (2017) VARX-L: Structured regularization for large vector autoregressions with exogenous variables. International Journal of Forecasting, 33(3), pp. 627-651.

Polbin, A., & Fokin, N. (2020). Modeling the dynamics of import in the Russian Federation using the error correction model. Applied Econometrics, 59, pp. 88-112.

Senhadji A. (1998) Time-series estimation of structural import demand equations: a cross-country analysis. Staff Papers, 45(2), pp. 236-268.

Senhadji A. (1999) Time series analysis of export demand equations: a cross-country analysis. IMF staff papers, 46(3), pp. 259-273.

Tibshirani R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), pp. 267-288.

Vedev A., Drobyshevsky S., Knobel A., Sokolov I., Trunin P. (2020) Scenarios of the development of economic situation in Russia in 2020–2021 and challenges for economic policy. Russian Economic Development, 27(5), pp. 4-23.

Zou H. H. (2005) Regularization and variable selection via the EN. Journal of the royal statistical society: series B (statistical methodology), 67(2), pp. 301-320.