Viktor P. Nevezhin, Anastasia V. Zhiglyaeva, Valery V. Smirnov and Natalya K. Muravitskaya

DOI: https://doi.org/10.6000/1929-7092.2019.08.66

Published: 02 October 2019

Abstract: The purpose of this study is to develop models to predict the level of innovative development of countries, as well as to identify the most significant factors influencing innovative development.

The scientific novelty consists in applying a systematic, integrated approach to the selection of statistically significant factors that are drivers of innovative development, with the subsequent construction of econometric models and their testing. When developing models, both resources (“input parameters”) and results (“output parameters”) were taken into account, which also allows evaluating the effectiveness of innovative development and developing scenario forecasts taking into account the existing possibilities and limitations, optimizing innovative development strategies.

The main methods of research and approaches were used: statistical summary and grouping of information, trend analysis, regression and correlation analysis, testing of statistical hypotheses, factor analysis. The procedure for detecting multicollinearity was performed using the VIF test (Variance Inflation Factor, incremental regression method). In determining the set of explanatory variables (the choice of "short" or "long" regression), the following criteria were used: Akaike criterion and Bayesian Schwarz information criterion. To estimate the parameters of econometric models, the Least Squares Method was used with a preliminary check of the fulfillment of all conditions of the Gauss-Markov theorem. In addition, various tests for checking the constructed models and their parameters for significance, adequacy were applied: Durbin-Watson test, Sved-Eisenhart series method and Breush-Godfrey test, Helvig agreement test, Shapiro-Wilk test, Goldfeld-Quandt test and Spearman's rank correlation test. To determine the influence of explanatory factors on the explained factor, the average elasticity coefficients were calculated on the basis of linear regression as the best model based on the results of all tests.

Data and Empirical Analysis: The main components included in the calculation of the Global Innovation Index (GII) were selected for the study. Statistical data on them are published annually, which allows us to estimate the country’s place in international innovation development. The study identified four multiple econometric models: one linear and three non-linear. The value of the Global Innovation Index was chosen as an explained factor, and the indicators for the main groups in accordance with the GII structure were chosen as explanatory factors.

To achieve this goal, the following work was carried out, as reflected in this article: 1) an econometric analysis was performed based on a sample of 30 countries based on the 2018 Global Innovation Index report; 2) multiple regression models were built - linear, polynomial, hyperbolic and power; 3) with the use of special tests, a check for heteroscedasticity and autocorrelation of random residues was implemented; 4) the parameters and the obtained regressions were estimated for statistical significance and adequacy.

According to the results of the study, the model that best approximates the initial data was chosen. Using this regression, one can form scenario forecasts of the country's innovative development, for example, by predicting the values of individual factors using various modern methods of macroeconomic planning and forecasting. The principle is the expediency of the most optimal combination of resources for innovative development in order to ensure the maximum effect on the "output".

Keywords: Econometric model, explanatory factors, explained factor, Global Innovation Index, innovative development, regression, forecasting.

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Andrey P. Kovaltchuk, Ekaterina A. Blinova, Konstantin A. Miloradov and Lyaylya S. Mangusheva

DOI: https://doi.org/10.6000/1929-7092.2019.08.67

Published: 02 October 2019

Abstract: Motivation: The substantiation of the scientific problem and the practical value of the study are determined by the enhanced cooperation of the BRICS countries, in particular the growing weight of Russia and China on the world stage. The main objective of the study: is to analyze the problems of the development of bilateral trade between China and Russia and to provide a statistically proven forecast for this trade for 2019-2020, as well as to develop recommendations for the improvement of bilateral trade relations. Novelty: The author’s statistical model was developed and its testing was presented through confirming the 24-month forecast of bilateral trade between Russia and China. The model involves solving various problems of bilateral trade. Recommendations for improvement of bilateral trade relations are proposed through formation of an investment and innovation model of bilateral trade.

Methodology and Methods: The work used the method of forecasting time series, which suggested the use of a model to predict future values based on previously observed values. To evaluate the modern prospects of the trade between the countries, the authors produced a forecast of the goods turnover trend for 2019-2020. The forecast was issued via the software tool Statgraphics Centurion 18. A reasonable model of the 24-month forecast based on the statistical model Random Walk is developed. The adequacy of the proposed forecast model was subjected to statistical tests. To verify the statistical adequacy of the model the relevant tests were done to determine the compliance of the model with the informational criteria ME (Mean Error), MSE (Mean Squared Error), МАЕ (Mean Absolute Error), МАРЕ (Mean Absolute Percentage Error), МРЕ (Mean Percentage Error). However, it should be noted that the forecast was issued in accordance with the trends which had been identified in the preceding periods. Data and empirical analysis: The factors influencing bilateral trade are analyzed, as well as examples of implemented projects of international cooperation between Russia and China are presented. The current dynamics of sales turnover between Russia and China for the period of 2010-2018 with the use of various statistical and analytical methods is studied, and a reasonable model of the 24-month forecast based on the statistical model Random Walk is developed. The adequacy of the proposed forecast model was subjected to statistical tests. The basic hypothesis is suggested for the upward trend based on reference time series. Policy considerations: It can be said with certainty that the level of technological development of BRICS countries will help Russia and China to start building their cooperation in many fields at a completely new level, taking into account their joint experience in overcoming global crises and Western sanctions. International cooperation between Russia and China in the innovation field will help them unite their efforts and achieve significant synergy. Coordination of countries on this issue will help to reduce production costs, cooperation of production, joint research and development, as well as increase bilateral trade turnover.

Keywords: Key indicators of BRICS countries development, the structure of trade between Russia and China, a forecast of mutual trade development for 2019-2020, an investment and innovation model, trade development prospects.

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