A Predictive Model for Institutional Innovation Index: Insights from Global Innovation Analysis

Abstract

This study presents a novel predictive model to estimate the Institutional Innovation Index of a country, using the multiple linear regression technique. Empirical data from the 2022 Global Innovation Index of the top 100 most innovative countries were utilized. The resulting model is expressed by the equation IIntsC = 12.89 + 0.69X2 − 0.21X1 + 0.067X5. The adjusted coefficient of determination (R2) is 0.8278, demonstrating its robust predictive capability. Additionally, it rigorously satisfies the fundamental assumptions of linear regression, supporting the validity and validity of the results. The presence of multicollinearity is discarded through an analysis of the variance inflation factor (VIF), homoscedasticity is checked via the Breusch-Pagan test, and the absence of autocorrelation is verified using the Durbin-Watson statistic. The model represents a significant advancement in the quantitative understanding of the institutional determinants of innovation at the country level, providing an analytical tool for the formulation of public policies and strategic planning aimed at strengthening institutional frameworks that promote innovation, a fundamental pillar for the economic growth and sustainable development of countries.