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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