Use of Machine Learning Methods to Forecast Investment in Russia

Mikhail Gareev

doi:10.31477/rjmf.202001.35

Abstract

This work forecasts the growth rate of quarterly gross fixed capital formation in Russia using machine learning methods (regularisation methods, ensemble methods) over a horizon of up to eight quarters. The methods tested show higher quality in terms of RMSFE than that of simple alternative models (autoregressive model, random walk model), with ensemble methods (boosting and random forest) leading in quality. The last statement is consistent with the results of other research on the application of big data in macroeconomics. It was found that removing observations from the sample which relate to the time before the 1998 crisis and that are atypical for the subsequent period of time does not worsen the short-term forecasts of machine learning methods. Estimates of the coefficients of generally accepted key investment factors obtained using regularisation methods are, on the whole, consistent with economic theory. The forecasts of the author’s models are superior in quality to the annual forecasts of growth rates of gross fixed capital formation published by the Ministry of Economic Development.

Highlights

Huge arrays of macroeconomic and financial data are available, and many applied studies in economic research come down to retrieving useful information from them
This was to be expected; poorly interpretable machine learning methods often demonstrate better results in terms of forecast quality in comparsion with regularisation methods. This pattern was seen in Baybuza (2018), where random forest and boosting methods turned out to be the winners for almost all specifications in the course of inflation forecasting, or in Kvisgaard (2018), where regularisation methods performed worse than other machine learning methods in the context of GDP and inflation forecasting
Selecting different values for the N parameter produces an insignificant deviation in the forecasting quality of the random forest model, which allows us to assert that a further increase in the number of decision trees will not improve the quality of predictions

Summary

Introduction

Huge arrays of macroeconomic and financial data are available, and many applied studies in economic research come down to retrieving useful information from them. Extensive research has been dedicated to the application of various methods for working with big data volumes in macroeconomics. Researchers must understand how adequate predictive models are and how consistent they are with economic theory, so that the use of regularisation can be justified (i.e. penalising inadequately high coefficients on predictors). Regularisation methods like LASSO (Least Absolute Shrinkage and Selecting Operator) or Ridge, or their modifications, on the one hand, allow the mitigation of risks associated with retraining the models to some extent, and, on the other hand, they preserve interpretability. Very often methods with weak interpretability tend to demonstrate the highest quality of forecasts in applied research (e.g., ensemble methods like boosting and random forest)

Methods

Results

Conclusion