Modeling Ridge–GLS Regression for Multicollinearity and Autocorrelation in the Consumer Price Index - Radifa Radifa; Nur’eni Nur’eni; Nurul Fiskia Gamayanti; Mohammad Fajri; Iman Setiawan; Fadjryani Fadjryani (Jurnal Varian, 2026)
The Consumer Price Index (CPI) in Indonesia showed a relatively stable increase from January 2022 to December 2023, but experienced fluctuations from January 2024 to February 2025. This situation highlights the need to analyze the factors influencing CPI movements. However, the data used in this study suffer from autocorrelation and multicollinearity issues. The aim of this study is to model the Consumer Price Index (CPI) in Indonesia and evaluate the effectiveness of Ridge Regression and Generalized Least Squares (GLS) in addressing multicollinearity and autocorrelation issues. The method used in this study combines Ridge Regression and Generalized Least Squares (GLS) with the Cochrane–Orcutt transformation. Ridge Regression is applied using the Hoerl, Kennard, and Baldwin (HKB) bias constant, while GLS is used through the Cochrane–Orcutt approach. This study uses monthly time-series data from January 2022 to February 2025, with independent variables consisting of narrow money supply, broad money supply, interest rates, and the Food Price Index (FPI). The results indicate that all Variance Inflation Factor (VIF) values were below 10 and the Durbin–Watson statistic increased to 1.9044. Additionally, broad money supply, narrow money supply, interest rates, and the Food Price Index were found to influence CPI. These findings indicate that the combined Ridge–GLS approach provides an appropriate model for analyzing Indonesian CPI dynamics in the presence of multicollinearity and autocorrelation.