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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Vanisa, Davina Shafa
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Prediction of Dow Jones Index, US Inflation, and Interest Rate with Kernel Estimator and Vector Error Correction Model Mardianto, M. Fariz Fadillah; Syahzaqi, Idruz; Permana, Made Riyo Ary; Makhbubah, Karina Rubita; Vanisa, Davina Shafa; Afifa, Fitriana Nur
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28460

Abstract

The Dow Jones Industrial Average (DJIA) is the oldest running U.S. stock market index, established by Dow Jones & Company under Charles Dow. Comprising thirty major publicly traded companies, the DJIA is a key indicator of macroeconomic health, reflecting investor confidence and economic stability. This study applies a quantitative research approach to forecast DJIA stock prices, inflation, and U.S. interest rates using time series analysis. Two forecasting methods are compared: Vector Error Correction Model (VECM) and Kernel regression. VECM, a parametric approach, estimates both short- and long-term relationships among economic variables, while Kernel regression, a nonparametric technique, effectively captures complex, nonlinear relationships without strict model assumptions. The results indicate that the Gaussian Kernel method provides the most accurate predictions, achieving a Mean Absolute Percentage Error (MAPE) of 5.72%. The analysis also shows that despite annual fluctuations, the DJIA has exhibited a steady growth trend from 2009 to 2024, with both its starting and ending prices increasing over time. This research is significant for investors, policymakers, and financial analysts, offering insights into market trends and economic indicators. By providing a reliable forecasting model, it aids in better decision-making regarding stock market investments and economic policies.
PROVINCIAL SEGMENTATION IN INDONESIA: EXPLORING FACTORS INFLUENCING EDUCATION WITH SEM-PLS METHOD, INCORPORATING MODERATION EFFECTS AND FIMIX-PLS APPROACH Vanisa, Davina Shafa; Rahmanita, Tentri Ryan; Ana, Elly; Kurniawan, Ardi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1955-1962

Abstract

The significance of education as a developmental metric is underscored by its designation as the 4th goal in the SDGs, which emphasizes ensuring inclusive, equitable, and high-quality education while also expanding lifelong learning opportunities for all. This research relies on two primary sources: secondary data from publications by the Indonesian Central Statistics Agency (BPS RI) in 2023 and the BPS website. The educational variables examined in this study are believed to be influenced by latent variables, including school performance, infrastructure, and poverty levels. Employing the Finite Mixture Partial Least Squares (FIMIX-PLS) approach, the research identified 13 valid and reliable indicators of educational variables. It delineated three regional groups based on the lowest BIC and CAIC values. In this structural equation research, the moderation effect is seen in the significance of the indirect relationship, especially the influence of Regional Poverty on Education with School Outcomes as a moderating construct.
Co-Authors Afifa, Fitriana Nur Ardi Kurniawan Elly Ana M. Fariz Fadillah Mardianto Makhbubah, Karina Rubita Permana, Made Riyo Ary Rahmanita, Tentri Ryan Syahzaqi, Idruz