Impact of Sub-Indices of GCI on GCI: Indian Evidence

Abstract

Competitiveness has become one of the important factors for economic growth Nowadays, Nations place more emphasis on increasing their competitiveness. This study aims to measure the effect of GCI sub-indices on GCI in India over the most recent 10 years, starting in 2008 and ending in 2017. The analysis first examines the data's normality and stationarity.  multiple regression method is used to measure the effect of GCI sub-indices on GCI. A correlation matrix, VIF, and TOL are used in the study to check for multicollinearity issues. The study also uses the Cusum test to determine whether the regression parameters are stable and several residuals’ tests to determine the model's validity. The investigation shows that the variable is not stationary. However, the first difference operator's ADF and PP tests make it stationary, the parameters of the model are stable and the coefficients of the regression equation are statistically significant. This indicates that the GCI score depends on its sub-indices. Additionally, it notes that there is no serious multicollinearity issue but there is an autocorrelation and heteroscedasticity issue in the residuals.