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 nonlinear. 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 significa...

According to Meadows' model, the main factor that determines the limits to the growth of the human civilization is the agricultural sector of the planet, the latter, however, significantly pollutes the environment and togather with other factors contributes much to global warming. A half of all the habitable land is used for agriculture. Unless the efficiency of agriculture is fundamentally improved and the amount of waste generated as a result, is reduced, a global catastrophe may befall in 30–50 years. Whereas agrarian “garbage” may not be just the waste that pollutes the environment, it can decrease the burden on the environment by being the raw material for fertilizers, feed or fuel manufacturing. Modern digital technologies can improve the efficiency of agriculture, organize low-waste or non-waste production and that will enable people to diminish the pollution of the environment and push away the limits to the growth of human civilization. The developed countries are using digital technologies more and more intensively to increase agricultural productivity and, at the same time, reduce both environmental pollution with agricultural waste and disruption of the ecological balance. The digitalization of agricultural business, the use of geoinformation technologies, drones, robots, artificial intelligence and other technologies of the digital society help to push the limits to the growth of human civilization away into the future.

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Real-time monitoring of the state of ecological systems can contribute to early warning of their deviation from an equilibrium state (homeostasis) or a change that leads to a threat to human health or existence. In addition to the existing means of monitoring the state of ecological systems and models for predicting the assessment of their state in the future, it is proposed to use models of the frequency characteristics of these systems, monitoring of which can detect signals about the appearance of unwanted deviations from homeostasis in the form of a change in the frequency spectrum. A change in the frequency spectrum can be converted into the sound waveform, which will allow timely detection of this undesirable change in the state of the ecological system. As a new information channel and analysis of the dynamics of the state of ecological systems, in the article it is proposed to use the wavelet transform of time series with the subsequent translation of the totality of their harmonic vibrations into sound form. In contrast to the Fourier transform, in which the spectrum of stationary and non-stationary processes is practically indistinguishable and it is impossible to determine the moment of the appearance of a new harmonic, the wavelet transformation gives this opportunity. In addition to the purely utilitarian application of the conversion of the vibrational characteristics of an ecological system into sound form, it becomes be possible to convert them into the "music" of ecological systems, which may give a new direction for creative understanding of the state of nature.

The article deals with the issues of sales in the passenger car market and the factors that influence these sales. The main trends of the automotive market of new cars are studied. The reasons that contribute to the growth of prices for new cars are determined. On the basis of statistical data on car prices for the last four years, an econometric model of the time series is constructed, taking into account seasonal changes in sales. The obtained model and its application possibilities for obtaining a forecast of the average price of a middle-class passenger car in the short term are considered. Based on this model, a forecast of the average price for four periods is made, which is confirmed by the actual values of prices for middle-class cars in 2019.