Penerapan Modified Jackknife Kibria-Lukman Regression dengan Koreksi Autokorelasi Prais-Winsten pada Nilai Tukar Rupiah terhadap Dolar Amerika Serikat - Chrisadna Patricia Kabangnga; A. Muthiah Nur Angriany; Raupong Raupong (Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi, 2026)
The movement of the Indonesian Rupiah exchange rate against the United States Dollar (USD) experienced volatility during the 2021-2024 period, thus requiring precise analysis to identify the factors influencing it. This modeling can be conducted through multiple linear regression analysis; however, parameter estimation using Ordinary Least Squares (OLS) is often inefficient and prone to producing large variances due to the violation of assumptions in the form of multicollinearity and autocorrelation. Therefore, this study aims to model the Rupiah exchange rate against the USD for the 2021–2024 period and identify the significantly influencing factors using the Modified Jackknife Kibria-Lukman Regression (MJKLR) method with Prais-Winsten (PW) autocorrelation correction. Autocorrelation handling was performed through the PW correction, followed by MJKLR modeling on the PW-transformed data to reduce the impact of multicollinearity. The results showed that the MJKLR-PW estimator provided a more efficient performance compared to OLS-PW and KLR-PW, with an estimator MSE of 0.1877, RMSE of 326.1730, and an adjusted R² value of 72.54%. The variables of money supply, interest rate, total exports, and total imports had a significant effect on the Rupiah exchange rate at a 5% significance level. In conclusion, the combination of MJKLR and PW is effective in modeling the Rupiah exchange rate against the USD that has autocorrelation and multicollinearity problems. Empirically, this study indicates that the stability of the Rupiah exchange rate relies heavily on macroeconomic fundamentals, particularly monetary policy and the trade balance.