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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Published in:HELIYON
Main Authors: Ali, Megat Syahirul Amin Megat; Zabidi, Azlee; Tahir, Nooritawati Md; Yassin, Ihsan Mohd; Eskandari, Farzad; Saadon, Azlinda; Taib, Mohd Nasir; Ridzuan, Abdul Rahim
Format: Article
Language:English
Published: CELL PRESS 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001193874400001
author Ali
Megat Syahirul Amin Megat; Zabidi
Azlee; Tahir
Nooritawati Md; Yassin
Ihsan Mohd; Eskandari
Farzad; Saadon
Azlinda; Taib
Mohd Nasir; Ridzuan
Abdul Rahim
spellingShingle Ali
Megat Syahirul Amin Megat; Zabidi
Azlee; Tahir
Nooritawati Md; Yassin
Ihsan Mohd; Eskandari
Farzad; Saadon
Azlinda; Taib
Mohd Nasir; Ridzuan
Abdul Rahim
Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
Science & Technology - Other Topics
author_facet Ali
Megat Syahirul Amin Megat; Zabidi
Azlee; Tahir
Nooritawati Md; Yassin
Ihsan Mohd; Eskandari
Farzad; Saadon
Azlinda; Taib
Mohd Nasir; Ridzuan
Abdul Rahim
author_sort Ali
spelling Ali, Megat Syahirul Amin Megat; Zabidi, Azlee; Tahir, Nooritawati Md; Yassin, Ihsan Mohd; Eskandari, Farzad; Saadon, Azlinda; Taib, Mohd Nasir; Ridzuan, Abdul Rahim
Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
HELIYON
English
Article
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 x 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.
CELL PRESS

2405-8440
2024
10
4
10.1016/j.heliyon.2024.e26438
Science & Technology - Other Topics
Green Published, gold
WOS:001193874400001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001193874400001
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
container_title HELIYON
language English
format Article
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 x 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.
publisher CELL PRESS
issn
2405-8440
publishDate 2024
container_volume 10
container_issue 4
doi_str_mv 10.1016/j.heliyon.2024.e26438
topic Science & Technology - Other Topics
topic_facet Science & Technology - Other Topics
accesstype Green Published, gold
id WOS:001193874400001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001193874400001
record_format wos
collection Web of Science (WoS)
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