Econometric Models for Forecasting Innovative Development of the Country
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Notice bibliographique
Résumé
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 parametersâ€) and results (“output parametersâ€) 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".
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle