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Pemodelan Regresi Seemingly Unrelated Menggunakan Metode Maximum Likelihood pada Data Panel

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 2 (2025) · pp. 152–166

DOI
10.20956/ejsa.v6i2.34463
ISSN
2721-379X, 2721-3803
Published
2025-08-04
Publisher
Hasanuddin University
Language
en

Abstract

This study aims to model and predict the Human Development Index (HDI) values in South Sulawesi Province for the period 2014–2022 using a multiple linear regression approach with the Maximum Likelihood Estimation (MLE) method. Multiple linear regression analysis often encounters multicollinearity issues among independent variables; therefore, Principal Component Analysis (PCA) is employed as a dimensionality reduction technique to eliminate correlations among explanatory variables. In addition, due to the potential correlation of residuals among equations in a multivariate model, the Seemingly Unrelated Regression (SUR) approach is used, which is also estimated using the MLE method. The data utilized in this study is panel data, which offers advantages in obtaining more comprehensive and accurate information regarding the relationships between the analyzed variables. The estimation results of the SUR model indicate that variables such as Life Expectancy (UHH), Mean Years of Schooling (RLS), Expected Years of Schooling (HLS), and Adjusted Per Capita Expenditure have a significant influence on HDI across all districts/cities in South Sulawesi. One of the estimated equations from the SUR model is y22t=81.44+0.670KU122 which illustrates the relationship between the principal component and HDI in a specific region.

Keywords

Collaboration network of the authors

40 authors · 115 co-authorship links · showing the 40 most connected

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  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anisa AnisaHasanuddin University7
Anna IslamiyatiHasanuddin University6
Erna Tri HerdianiHasanuddin University4
A. Muthiah Nur AngrianyHasanuddin University4
Nirwan IlyasHasanuddin University3
Georgina Maria TinungkiHasanuddin University3
Saleh AfAffiliation unknown3
Nurtiti SunusiHasanuddin University2
Andi Kresna JayaHasanuddin University2
Zhazha Alifkhamulki RamdhaniHasanuddin University1
Samsul ArifinHasanuddin University1
Andi Sitti Fahmi Riyanti HufainiHasanuddin University1
Dwicahyo Ramadhan PriyatnaHasanuddin University1
La Podje TalangkoHasanuddin University1
Vieri KoerniawanHasanuddin University1
Iis Cendrah KasihHasanuddin University1
Dewi Rahma EnteHasanuddin University1
Andi Tenri Riski AmaliaAffiliation unknown1
Reski AmalahAffiliation unknown1
Sitti SahrimanHasanuddin University1
Fitri AnnisaAffiliation unknown1
Ratmila MammiAffiliation unknown1
Nur Alfianingsih RajaHasanuddin University1
Muhammad FadilAffiliation unknown1
Anisa KalondengHasanuddin University1
Nurkamalia NurkamaliaAffiliation unknown1
Sitti SahrimanAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Asnidar AsnidarHasanuddin University1
Nuralyatussa’ AdaAffiliation unknown1
Parida Ayu NingsiAffiliation unknown1
Fauziah NurhidayahAffiliation unknown1
Anni Ivoni ParapaHasanuddin University1
Muhammad ZakirHasanuddin University1
Muhammad Jayzul UsrahHasanuddin University1
Ummi SariHasanuddin University1
Fajar AffanHasanuddin University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Pemodelan Regresi Seemingly Unrelated Menggunakan Metode Maximum Likelihood pada Data Panel</dc:title>
	<dc:creator xml:lang="en">Hikmah, Nurul</dc:creator>
	<dc:creator xml:lang="en">Raupong, Raupong</dc:creator>
	<dc:creator xml:lang="en">Sirajang, Nasrah</dc:creator>
	<dc:subject xml:lang="en">Linear Regression</dc:subject>
	<dc:subject xml:lang="en">Maximum Likelihood Estimation</dc:subject>
	<dc:subject xml:lang="en">Seemingly Unrelated Regression</dc:subject>
	<dc:subject xml:lang="en">PCA</dc:subject>
	<dc:subject xml:lang="en">Human Development Index</dc:subject>
	<dc:description xml:lang="en">This study aims to model and predict the Human Development Index (HDI) values in South Sulawesi Province for the period 2014–2022 using a multiple linear regression approach with the Maximum Likelihood Estimation (MLE) method. Multiple linear regression analysis often encounters multicollinearity issues among independent variables; therefore, Principal Component Analysis (PCA) is employed as a dimensionality reduction technique to eliminate correlations among explanatory variables. In addition, due to the potential correlation of residuals among equations in a multivariate model, the Seemingly Unrelated Regression (SUR) approach is used, which is also estimated using the MLE method. The data utilized in this study is panel data, which offers advantages in obtaining more comprehensive and accurate information regarding the relationships between the analyzed variables. The estimation results of the SUR model indicate that variables such as Life Expectancy (UHH), Mean Years of Schooling (RLS), Expected Years of Schooling (HLS), and Adjusted Per Capita Expenditure have a significant influence on HDI across all districts/cities in South Sulawesi. One of the estimated equations from the SUR model is y22t=81.44+0.670KU122 which illustrates the relationship between the principal component and HDI in a specific region.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-08-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/34463</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i2.34463</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 2, Juli, 2025 : Estimasi; 152-166</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/34463/13219</dc:relation>
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