Abstract
The article presents a study of the influence of artificial deep neural networks (AGNN) on increasing the accuracy and reliability of predictive regression models in the economics of the scientific sector of 61 countries, including Russia. We integrated the feedforward IGNS into the econometric discrete choice model (DCM), which represents a multinomial logit model (MNLR). AGNN in regression models built on statistical data with a large range of standard deviation lead to an increase in forecasting accuracy by at least 32% within 0,8 periods of the analyzed data. The use of integration of AGNN with DCM is limited only to heterogeneous data. The structures under consideration can effectively solve econometric problems and problems of managing organizational systems.
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