Article Sidebar

Submitted
Aug 26, 2024
Accepted
Jul 24, 2025
Published
Jul 27, 2025
Download
PDF
Statistic
Read Counter : 3 Download : 3

Main Article Content

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.

Keywords

Spatial Regression, Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), Life Expectancy

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Hafid, H., Ayu Pebriyanti, & Sudarmin. (2025). Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) Modeling on Life Expectancy Data in South Sulawesi Province 2022. Parameter: Journal of Statistics, 5(1), 13-26. https://doi.org/10.22487/27765660.2025.v5.i1.17397

References

  1. Anselin, L. (2005). Spatial Regression Analysis in R: A Workbook. Center for Spatially Integrated Social Science. https://geodacenter.asu.edu/system/files/rex1.pdf
  2. Ardianti, A. V. (2015). Factors Influencing Life Expectancy in Jember Regency. [Thesis]. East Java: Faculty of Economics, University of Jember.
  3. Aswi & Sukarna (2006). Time Series Analysis: Theory and Application. Makassar: Andira Publisher.
  4. Ayuni, N. W. D. (2015). Modeling Life Expectancy in East Java Province Using Feed Forward Neural Network (FFNN) Method. Journal of Social and Humanities, 5(2), 103–113.
  5. BPS. (2016). Data and Information on Poverty in Districts Cities for the Year 2016. Data and Information on Poverty in Districts or Cities for the Year 2016, 185.
  6. Dewi, D. K., Azizah, A., & Widodo, E. (2019). Comparison of Multiple Regression, Spatial Autoregressive, and Spatial Error Models on Malnutrition in Indonesia for the Year 2017. Proceedings-M5.
  7. Fauzi, F. F., Gabriella Hilary Wenur, & Rochdi Wasono. (2023). Spatial Durbin Model of Unemployment Rate in Central Java. Parameter: Journal of Statistics, 3(1), 7–18. https://doi.org/10.22487/27765660.2023.v3.i1.16423
  8. Griffith, D. A., & Anselin, L. (1989). Spatial Econometrics: Methods and Models. In Economic Geography (Vol. 65, Issue 2). https://doi.org/10.2307/143780
  9. Islamy, U., Novianti, A., Hidayat, F. P., & Kurniawan, M. H. S. (2021). Application of the Spatial Autoregressive (SAR) Method in Analyzing Poverty in Indonesia and the Self Organizing Map (SOM) Method in Grouping Provinces Based on Factors Affecting Poverty. Enthusiastic : International Journal of Applied Statistics and Data Science, 1(2), 76–83. https://doi.org/10.20885/enthusiastic.vol1.iss2.art4
  10. LeSage, J. P. (1999). The Theory and Practice of Econometrics. New York: University of Toledo. https://doi.org/10.2307/2553707
  11. LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. In CRC Press. New York: Taylor & Francis Group. https://doi.org/10.1201/9781420064254
  12. Mukrom, M. H., Yasin, H., & Hakim, A. R. (2021). Modeling Life Expectancy in Central Java Province Using the Robust Spatial Durbin Model. Gaussian Journal, 10(1), 44–54. https://doi.org/10.14710/j.gauss.v10i1.30935
  13. Rahmawati, D., & Bimanto, H. (2021). Comparison of the Spatial Autoregressive Model and Spatial Error Model in Modeling the Human Development Index in Java Province. Journal of Statistics and its Applications, 5(1), 41–50.
  14. Rinaldi, A., Susianto, Y., Santoso, B., & Kusumaningtyas, W. (2021). Spatial Modeling for Poverty: The Comparison of Spatial Error Model and Geographic Weighted Regression. Al-Jabar : Journal of Mathematics Education, 12(1), 237–251. https://doi.org/10.24042/ajpm.v12i1.8671
  15. Safitri, I. Y. (2020). Spatial Regression Analysis to Exmine Factors Influencing Food Security at the District Level in South Sulawesi Province. [Thesis]. Makassar: Faculty of Mathematics and Natural Sciences, State University of Makassar.
  16. Sunandi, E., Sumertajaya, I. M., Djuraidah, A., Wigena, A. H., & Fitrianto, A. (2021). Dynamic Spatial Durbin Modeling (Case: Percentage of Poor Population in Indonesia 2010-2019). Journal of Physics: Conference Series, 1863(1), 1–12. https://doi.org/10.1088/1742-6596/1863/1/012046
  17. Wasono, R., Karim, A., Darsyah, M. Y., & Suwardi. (2018). Planning the School Operational Assistance (BOS) Program in Central Java Province Based on the Spatial Autoregressive (SAR) Model and Spatial Error Model (SEM). National Seminar on Edusaintek, 2012, 1–4.
  18. Yasin, H., Warsito, B., & Hakim, A. R. (2020). Spatial Regression (Application with R). WADE Group.
  19. Yuriantari, N. P., Hayati, M. N., & Wahyuningsih, S. (2017). Spatial Autocorrelation Analysis of Hotspots in East Kalimantan Using Moran's Index and Local Indicator of Spatial Autocorrelation (LISA). Eksponential Journal, 8(1), 63–70. http://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/78

DB Error: Unknown column 'Array' in 'where clause'