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Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model

Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is...

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Main Authors:	Megat Syahirul Amin, Megat Ali, Azlee, Zabidi, Nooritawati, Md Tahir, Ihsan, Mohd Yassin, Eskandari, Farzad, Azlinda, Saadon, Mohd Nasir, Taib, Abdul Rahim, Ridzuan
Format:	Article
Language:	English
Published:	Elsevier Ltd 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/40980/1/Short-term%20Gini%20coefficient%20estimation%20using%20nonlinear%20autoregressive.pdf http://umpir.ump.edu.my/id/eprint/40980/ https://doi.org/10.1016/j.heliyon.2024.e26438 https://doi.org/10.1016/j.heliyon.2024.e26438
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spelling	my.ump.umpir.409802024-05-28T08:12:44Z http://umpir.ump.edu.my/id/eprint/40980/ Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model Megat Syahirul Amin, Megat Ali Azlee, Zabidi Nooritawati, Md Tahir Ihsan, Mohd Yassin Eskandari, Farzad Azlinda, Saadon Mohd Nasir, Taib Abdul Rahim, Ridzuan QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987–2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10−7) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches. Elsevier Ltd 2024-02-29 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40980/1/Short-term%20Gini%20coefficient%20estimation%20using%20nonlinear%20autoregressive.pdf Megat Syahirul Amin, Megat Ali and Azlee, Zabidi and Nooritawati, Md Tahir and Ihsan, Mohd Yassin and Eskandari, Farzad and Azlinda, Saadon and Mohd Nasir, Taib and Abdul Rahim, Ridzuan (2024) Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model. Heliyon, 10 (e26438). pp. 1-19. ISSN 2405-8440. (Published) https://doi.org/10.1016/j.heliyon.2024.e26438 https://doi.org/10.1016/j.heliyon.2024.e26438
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Megat Syahirul Amin, Megat Ali Azlee, Zabidi Nooritawati, Md Tahir Ihsan, Mohd Yassin Eskandari, Farzad Azlinda, Saadon Mohd Nasir, Taib Abdul Rahim, Ridzuan Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
description	Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987–2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10−7) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches.
format	Article
author	Megat Syahirul Amin, Megat Ali Azlee, Zabidi Nooritawati, Md Tahir Ihsan, Mohd Yassin Eskandari, Farzad Azlinda, Saadon Mohd Nasir, Taib Abdul Rahim, Ridzuan
author_facet	Megat Syahirul Amin, Megat Ali Azlee, Zabidi Nooritawati, Md Tahir Ihsan, Mohd Yassin Eskandari, Farzad Azlinda, Saadon Mohd Nasir, Taib Abdul Rahim, Ridzuan
author_sort	Megat Syahirul Amin, Megat Ali
title	Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
title_short	Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
title_full	Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
title_fullStr	Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
title_full_unstemmed	Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
title_sort	short-term gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
publisher	Elsevier Ltd
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/40980/1/Short-term%20Gini%20coefficient%20estimation%20using%20nonlinear%20autoregressive.pdf http://umpir.ump.edu.my/id/eprint/40980/ https://doi.org/10.1016/j.heliyon.2024.e26438 https://doi.org/10.1016/j.heliyon.2024.e26438
_version_	1822924364981469184
score	13.235362

Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model

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