Spatial Panel Regression Modelling of Rainfall in Indonesia

Fang You Dwi Ayu Shalu Saniyawati; Suci Astutik; Henny Pramoedyo

doi:10.31764/jtam.v9i2.29882

Spatial Panel Regression Modelling of Rainfall in Indonesia

Fang You Dwi Ayu Shalu Saniyawati, Suci Astutik, Henny Pramoedyo

Abstract

Rainfall is amount of water that falls to the earth's surface in the form of rain during a certain period of time, usually measured in millimeters. Rainfall data in Indonesia usually includes temporal and spatial dimensions, so the appropriate method for its analysis is spatial panel regression analysis. This study aims to identify factors that influence the amount of rainfall in Indonesia. This type of research is quantitative using secondary data from the central statistics agency website. The predictor variables used include air temperature, sunshine radiation, humidity, wind speed, and air pressure, while the response variable is amount of rainfall in 34 provinces in Indonesia. Spatial panel regression analysis is carried out using maximum likelihood estimation, which is used to estimate the regression coefficient and intercept that maximizes the likelihood of the existing data. Based on the lagrange multiplier test, spatial autocorrelation was found in the lag, so the appropriate model is SAR-FE. This model can overcome spatial autocorrelation by taking into account spatial interactions between locations, as well as controlling unobserved heterogeneity through fixed effects. The results show that sunshine radiation, humidity, and wind speed have significant effect on the amount of rainfall in Indonesia. The AIC value of SAR-FE model (-4.352594×〖10〗^(-13)) is smaller than SEM-FE model (-1.642001×〖10〗^(-12)), indicating that SAR-FE model is better at explaining the data.

Keywords

Spatial; Spatial Panel; Regression; Rainfall.

Full Text:

DOWNLOAD [PDF]

References

Ajr, E. Q., & Dwirani, F. (2019). Menentukan Stasiun Hujan dan Curah Hujan dengan Metode Polygon Thiessen Daerah Kabupaten Lebak. JURNALIS (Jurnal Lingkungan Dan Sumberdaya Alam), 2(2), 139–146. https://ejournal.lppm-unbaja.ac.id/index.php/jls/article/view/674/387

Alita, D., Putra, A. D., & Darwis, D. (2021). Analysis of Classic Assumption Test and Multiple Linear Regression Coefficient Test For Employee Structural Office Recommendation. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(3), 295–306. https://doi.org/10.22146/ijccs.65586

Annur, M. F. (2019). Analisis Faktor Indeks Pembangunan Manusia di Kabupaten Landak Menggunakan Model Regresi Spasial. RIEMANN (Research of Mathematics and Mathematics Education), 1(1), 44–54. https://doi.org/10.38114/riemann.v1i1.14

Arif, A., Tiro, M. A., & Nusrang, M. (2019). Metode Boostrap dan Jackknife dalam Mengestimasi Parameter Regresi Linear Ganda (Kasus: Data Kemiskinan Kota Makassar Tahun 2017). VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 1(2), 66–76. https://doi.org/10.35580/variansiunm12895

Bayar, Y., Gavriletea, M. D., & Ucar, Z. (2018). Financial Sector Development, Openness, and Entrepreneurship: Panel Regression Analysis. Sustainability (Switzerland), 10(10), 1–11. https://doi.org/10.3390/su10103493

Belotti, F., Hughes, G., & Mortari, A. P. (2017). Spatial Panel-Data Models using Stata. Stata Journal, 17(1), 139–180. https://doi.org/10.1177/1536867x1701700109

Cheung, T. K. Y., Wong, W. hung, Zhang, A., & Wu, Y. (2020). Spatial Panel Model for Examining Airport Relationships within Multi-Airport Regions. Transportation Research Part A: Policy and Practice, 133(January), 148–163. https://doi.org/10.1016/j.tra.2019.12.011

Elhorst, J. P. (2014). Spatial Econometrics. In SpringerBriefs in Regional Science (pp. 1–119). https://doi.org/10.1007/978-3-642-40340-8_1

Gujarati, D. N. (2004). Basic Econometrics. In The McGraw-Hill Companies (pp. 1–1002). https://doi.org/10.1596/1813-9450-8096

Khatun, N. (2021). Applications of Normality Test in Statistical Analysis. Open Journal of Statistics, 11(1), 113–122. https://doi.org/10.4236/ojs.2021.111006

Klein, A. G., Gerhard, C., Büchner, R. D., Diestel, S., & Schermelleh-Engel, K. (2016). The Detection of Heteroscedasticity in Regression Models for Psychological Data. Psychological Test and Assessment Modeling, 58(4), 542–568. https://www.researchgate.net/publication/311518028

Lakshmanasamy. (2015). The Effect of Monsoon Rainfall Shortage on Farmer Suicides in India - A Panel Data Econometric Analysis. The Indian Geographical Journal, 90(1), 14–25. www.igschennai.org

Roustaei, N. (2024). Application and Interpretation of Linear-Regression Analysis. Medical Hypothesis, Discovery, and Innovation in Ophthalmology, 13(3), 151–159. https://doi.org/10.51329/mehdiophthal1506

Stefko, R., Gavurova, B., Kelemen, M., Rigelsky, M., & Ivankova, V. (2021). Relationships between Renewable Energy and the Prevalence of Morbidity in the Countries of the European Union: A panel Regression Approach. International Journal of Environmental Research and Public Health, 18(12), 1–18. https://doi.org/10.3390/ijerph18126548

Tarigan, W. S. (2021). Analisis Regresi Spasial pada Indeks Pembangunan Manusia di Provinsi Sumatera Utara Tahun 2020. Seminar Nasional Official Statistics, 2021(1), 403–408. https://doi.org/10.34123/semnasoffstat.v2021i1.896

Trinh, T. A., Posso, A., & Feeny, S. (2020). Child Labor and Rainfall Deviation: Panel Data Evidence from Rural Vietnam. Developing Economies, 58(1), 63–76. https://doi.org/10.1111/deve.12215

Uyanto, S. S. (2022). Monte Carlo Power Comparison of Seven Most Commonly used Heteroscedasticity Tests. Communications in Statistics: Simulation and Computation, 51(4), 2065–2082. https://doi.org/10.1080/03610918.2019.1692031

Wang, H., & Yang, J. (2019). Total-Factor Industrial Eco-Efficiency and Its Influencing Factors in China: A Spatial Panel Data Approach. Journal of Cleaner Production, 227(1), 263–271. https://doi.org/10.1016/j.jclepro.2019.04.119

Yanti, N. K. J. F., Susilawati, M., & Suciptawati, N. L. P. (2023). Pemodelan Tingkat Kriminalitas di Provinsi Jawa Timur dengan Regresi Data Panel Spasial. Journal on Education, 6(1), 1508–1517. http://jonedu.org/index.php/joe

Yin, C., Yuan, M., Lu, Y., Huang, Y., & Liu, Y. (2018). Effects of Urban Form on the Urban Heat Island Effect based on Spatial Regression Model. Science of the Total Environment, 634(1), 696–704. https://doi.org/10.1016/j.scitotenv.2018.03.350

DOI: https://doi.org/10.31764/jtam.v9i2.29882