Use of Machine Learning Methods to Forecast Investment in Russia
Mikhail Gareev ()
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Mikhail Gareev: Russian Presidential Academy of National Economy and Public Administration (RANEPA)
Russian Journal of Money and Finance, 2020, vol. 79, issue 1, 35-56
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.
Keywords: investment forecasts; machine learning; LASSO; boosting; random forest (search for similar items in EconPapers)
JEL-codes: C53 E22 (search for similar items in EconPapers)
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