Development of an Ensemble of Models for Predicting Socio-Economic Indicators of the Russian Federation using IRT-Theory and Bagging Methods

Olga Kitova and Victoria Savinova

MPRA Paper from University Library of Munich, Germany

Abstract: This article describes the application of the bagging method to build a forecast model for the socio-economic indicators of the Russian Federation. This task is one of the priorities within the framework of the Federal Project "Strategic Planning", which implies the creation of a unified decision support system capable of predicting socio-economic indicators. This paper considers the relevance of the development of forecasting models, examines and analyzes the work of researchers on this topic. The authors carried out computational experiments for 40 indicators of the socio-economic sphere of the Russian Federation. For each indicator, a linear multiple regression equation was constructed. For the constructed equations, verification was carried out and indicators with the worst accuracy and quality of the forecast were selected. For these indicators, neural network modeling was carried out. Multilayer perceptrons were chosen as the architecture of neural networks. Next, an analysis of the accuracy and quality of neural network models was carried out. Indicators that could not be predicted with a sufficient level of accuracy were selected for the bagging procedure. Bagging was used for weighted averaging of prediction results for neural networks of various configurations. Item Response Theory (IRT) elements were used to determine the weights of the models.

Keywords: Socio-economic Indicators of the Russian Federation; Forecasting; Bagging; Multiple Linear Regression; Neural Networks; Item Response Theory (search for similar items in EconPapers)
JEL-codes: C45 (search for similar items in EconPapers)
Date: 2021-11-25
New Economics Papers: this item is included in nep-big, nep-cis, nep-cmp and nep-for
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