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Journal : Parameter: Journal of Statistics

SPATIAL AUTOREGRESSIVE MODEL (SAR) AND SPATIAL ERROR MODEL (SEM) MODELING ON LIFE EXPECTANCY DATA IN SOUTH SULAWESI PROVINCE 2022 Ayu Pebriyanti; Hafid, Hardianti; Sudarmin
Parameter: Journal of Statistics Vol. 5 No. 1 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i1.17397

Abstract

Spatial regression is a development of classical linear regression which takes into account the spatial or spatial effects of the data being analyzed. The Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) methods include spatial regression models show that spatial effects on response variables and predictor variables. This research aims to model the factors that influence life expectancy in South Sulawesi Province in 2022. The analysis method used in this research is the SAR and SEM methods. The results show that based on the Lagrange Multiplier test values, there are lag and error dependencies. Based on the research results, it was found that the SAR and SEM models each had Akaike’s Information Criterion (AIC) values of 94.0069 and 90.6410, so the best model for analyzing the influence life expectancy value was the SEM model because the smallest had Akaike’s Information Criterion (AIC) value was obtained. The factors that have a significant influence on life expectancy are average years of schooling and gross regional domestic product which have a positive effect. Then, the percentage of poor population and per capita expenditure have a negative effect.
APPLICATION OF THE COPULA FRANK FOR ESTIMATING VALUE AT RISK (VAR) IN TELECOMMUNICATION SUB SECTOR STOCKS Mutiara; Sudarmin; Hafid, Hardianti
Parameter: Journal of Statistics Vol. 5 No. 2 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i2.17823

Abstract

Investment is investing capital with the aim of getting money or additional profits. When investing, you need to pay attention to risks that can cause losses for investors. One method that is widely used to measure investment risk is Value at Risk (VaR). VaR often has limitations, especially in capturing non-linear dependencies between variables, so a copula function is needed that can handle moderate to strong dependencies. One of the copulas used is the Archimedian copula with Frank subcopula. This article aims to estimate investment risk using the Value at Risk (VaR) method based on the Frank copula approach and to analyze the dependency structure between stock returns. The main steps in estimating VaR using the Frank copula are calculating the return of each stock, estimating the parameters of the Frank copula, carrying out data simulations using Frank copula parameters, calculating the VaR value using Frank copula. The data used in this research comes from shares of PT. Telkom Indonesia Tbk and shares PT. Indosat Ooredoo Hutchison Tbk. These two stocks have a positive correlation of 0.136. However, such a low correlation may still indicate for non-linear dependencies or tail dependencies that cannot be captured by linear correlations, so additional analysis, namely Frank copula, is required. The estimated Frank copula parameter value is 0.825. From the VaR estimation results, the risk obtained at a 90% confidence level is -0.0222, at a 95% confidence level it is -0.0281 and at a 99% confidence level it is -0.0383.