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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam ESTIMASI: Journal of Statistics and Its Application Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Raupong, Raupong
Hasanuddin University
Computer Science & IT Education Mathematics

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Estimasi Parameter Model Regresi Data Panel Menggunakan Metode Least Square Dummy Variable NUR AMINAH AHMAD; Raupong Raupong
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27530

Abstract

Panel data regression is a set of techniques for modeling the effect of independent variable on the dependent variable of panel data. The parameter estimation in the panel data regression model used the least squares method, but the difference between the intercept and the slope could not be known between time and between cross-section. One of the methods used is the Least Square Dummy Variable method (LSDV). The LSDV method is a method that has the same stages as the least squares method, but uses dummy variable to get different intercept score. This research uses the LSDV method to explain the differences in intercept between cross-sections using balanced panel data, namely the Human Development Index (HDI) data in South Sulawesi 2011-2017 to get fixed effect panel data regression model parameters on that data and the regencies with Average Length of School (ALS) and Life Expectancy (LE) variable that has the most influence on HDI based on the coefficient of determination criteria. According to the results of this research, the score of the coefficient of determination in the panel data regression model using the fixed effect model in each cross-section (regency), there are also three regencies with the highest coefficient of determination, respectively, Gowa, Pare-pare and Bantaeng regency that ALS and LE are able to explain the HDI variables 98.942%, 98.089% and 97.444%.
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 Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.38482

Abstract

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.
Co-Authors A. Muthiah Nur Angriany Alfiana Wahyuni Andi Kresna Jaya Andi Sitti Fahmi Riyanti Hufaini Andi Tenri Riski Amalia Anisa Anisa Anna Islamiyati Anna Islamiyati Arifin, Samsul Asti Inayati Magfirah Chrisadna Patricia Kabangnga Dewi Rahma Ente Dwicahyo Ramadhan Priyatna Georgina Maria Tinungki Jusmawati Massalesse La Podje Talangko Mega Selvia Tjahaya Nasrah Sirajang Nirwan Ilyas Nur Aminah Ahmad NURTITI SUNUSI Sitti Sahriman Vieri Koerniawan Zhazha Alifkhamulki Ramdhani