Spatial Autoregressive Quantile Regression with Application on Open Unemployment Data
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Open Unemployment Level (OUL), Spatial effects, SAR, SARQR, The Outlier
Abstract
The Open Unemployment Level (OUL) is the percentage of the unemployed to the total labor force. One of the provinces with the highest OUL score in Indonesia is West Java Province. If an object of observation is affected by spatial effects, namely spatial dependence and spatial diversity, then the regression model used is the Spatial Autoregressive (SAR) model. Quantile regression minimizes absolute weighted residuals that are not symmetrical. It is perfect for use on data distribution that is not normally distributed, dense at the ends of the data distribution, or there are outliers. The Spatial Autoregressive Quantile Regression (SARQR) is a model that combines spatial autoregressive models with quantile regression. This research used the data regarding OUR in West Java in 2020 from the Central Bureau of Statistics. This study develops to modeling the Open Unemployment Level in all province in Indonesia using modified spatial autoregressive model with the quantile regression approach. This study compares the estimation results based on SAR and SARQR models to obtain an acceptable model. In this study, it was found that the SARQR model is better than SAR at dealing with the problems of dependency and diversity in spatial data modeling and is not easily affected by the presence of outlier data.
References
Candraningtyas, C. P. and A. B. Santosa (2022). Open Unemployment Rate Modeling in The Province of Jawa Timur in 2019-2021 using Data Panel Regression Method: Open Unemployment Rate Modeling in The Province of Jawa Timur in 2019-2021 using Data Panel Regression Method. Jurnal Forum Analisis Statistik (FORMASI), 2(2); 92–103
Central, B. S. (2021). Tingkat Pengangguran Terbuka (TPT) Jawa Barat sebesar 9,82 persen, volume 5. jabar bps
Dai, X. and L. Jin (2021). Minimum Distance Quantile Regression for Spatial Autoregressive Panel Data Models with Fixed Effects. Plos One, 16(12); e0261144
Dai, X., S. Li, L. Jin, and M. Tian (2022). Quantile Regression for Partially Linear Varying Coefficient Spatial Autoregressive Models. Communications in Statistics-Simulation and Computation, 7(9); 1–16
Dai, X., Z. Yan, M. Tian, and M. Tang (2020). Quantile Regression for General Spatial Panel Data Models With Fixed Effects. Journal of Applied Statistics, 47(1); 45–60
Davino, C., M. Furno, and D. Vistocco (2013). Quantile Regression: Theory and Applications, volume 988. John Wiley & Sons
Hapsari, A. C. N. and M. I. Hasmarini (2022). Analysis of The Determining Factors of Open Unemployment Rate In East Java Province 2018-2020. Eduvest-Journal of Universal Studies, 2(12); 2864–2871
Huang, H., M. A. Abdel Aty, and A. L. Darwiche (2010). County Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling. Transportation Research Record, 2148(1); 27–37
Jin, L., X. Dai, A. Shi, and L. Shi (2016). Detection of Outliers in Mixed Regressive-spatial Autoregressive Models. Communications in Statistics Theory and Methods, 45(17); 5179–5192
Lum, K. and A. E. Gelfand (2012). Spatial Quantile Multiple Regression Using the Asymmetric Laplace Process. Bayesian Analysis, 7; 235–258
McMillen, D. (2015). Conditionally Parametric Quantile Regression for Spatial Data: An Analysis of Land Values in Early Nineteenth Century Chicago. Regional Science and Urban Economics, 55; 28–38
Su, L. and Z. Yang (2011). Instrumental Variable Quantile Estimation of Spatial Autoregressive Models. Research Collection School Of Economics, 7; 1–35
Tribhuwaneswari, A. B., A. Hapsery, and W. K. Rahayu (2022). Spatial Autoregressive Quantile Regression as A Tool for Modelling Human Development Index Factors in 2020 East Java. AIP Publishing LLC, 2668(1); 070007
Ver Hoef, J. M., E. E. Peterson, M. B. Hooten, E. M. Hanks, and M. J. Fortin (2018). Spatial Autoregressive Models for Statistical Inference from Ecological Data. Ecological Monographs, 88(1); 36–59
Weisberg, S. (2014). Applied Linear Regression (Fourth Edition). NJ: Wiley
Xu, P. and H. Huang (2015). Modeling Crash Spatial Heterogeneity: Random Parameter Versus Geographically Weighting. Accident Analysis & Prevention, 75; 16–25
Yanuar, F., A. S. Deva, and A. Zetra (2023). Length of Hospital Stay Model of COVID-19 Patients with Quantile Bayesian with Penalty LASSO. Communications in Mathematical Biology and Neuroscience, 2023; Article–ID
Yanuar, F., H. Yozza, and A. Zetra (2022). Modified Quantile Regression For Modeling The Low Birth Weight. Frontiers in Applied Mathematics and Statistics, 2; 58
Yanuar, F. and A. Zetra (2021). Length of Stay of Hospitalized COVID-19 Patients Using Bootstrap Quantile Regression. IAENG International Journal of Applied Mathematics, 51(3); 1–12
Yanuar, F., A. Zetra, C. Muharisa, D. Devianto, A. R. Putri, and Y. Asdi (2019). Bayesian Quantile Regression Method to Construct the Low Birth Weight Model. Journal of Physics: Conference Series, 1245(1); 012044
Yasin, H., A. R. Hakim, and B. Warsito (2020). Development Life Expectancy Model in Central Java using Robust Spatial Regression with M-estimators. Communications in Mathematical Biology and Neuroscience, 69; 1–16
Yasin, H., B. Warsito, A. R. Hakim, and R. N. Azizah (2022). Life Expectancy Modeling Using Modified Spatial Autoregressive Model. Media Statistika, 15(1); 72–82
Yu, T., F. Gao, X. Liu, and J. Tang (2021). A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates. Sensors, 22(1); 5
Zhang, J., Q. Lu, L. Guan, and X. Wang (2021a). Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence From the Panel Data in China. Energies, 14(2); 504
Zhang, Y., J. Jiang, and Y. Feng (2021b). Penalized Quantile Regression for Spatial Panel Data With Fixed Effects. Communications in Statistics-Theory and Methods, 6; 1–13
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