Melola Discovery

← Melola Discovery

Jurnal

Analisis Data Produk Domestik Regional Bruto Pulau Jawa Menggunakan Pendekatan Regresi Kuantil Spasial

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 2 (2023) · pp. 153–164

DOI
10.20956/ejsa.v4i2.27573
ISSN
2721-379X, 2721-3803
Published
2023-08-04
Publisher
Hasanuddin University
Language
en

Abstract

Gross Regional Domestic Product (GRDP) often shows spatial patterns. In a spatial perspective, spatial effects consist of of spatial dependence and spatial heterogeneity. To address the problems, this study uses spatial autoregressive quantile regression/SARQR model. SARQR is a method that combines Spatial Autoregressive (SAR) modeling with quantile regression. There are two methods that can be used to estimate the parameters of the SARQR model, namely Two Stage Quantile Regression (2SQR) and Instrumental Variable Quantile Regression (IVQR). The simulation results showed that IVQR method is better than 2SQR method. IVQR provides a smaller value and variance of bias. Furthermore, IVQR method is applied to Java’s GRDP data on 2019. The results showed that the number of workers significantly influences Java’s GRDP. The highest quantile verification skill score (QVSS) value is 0.713 when τ =0.75. It means that in the 75% quantile modeling, the model can describe the GRDP diversity of 71.3%.

Keywords

Collaboration network of the authors

26 authors · 60 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • IPB University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kusman SadikAffiliation unknown2
I Made SumertajayaAffiliation unknown2
Anwar FitriantoAffiliation unknown2
Enny KristianiIPB University1
Ujang SumarwanIPB University1
Lilik Noor YuliatiIPB University1
Ismah IsmahAffiliation unknown1
Erfiani ErfianiAffiliation unknown1
Farah AbqorunnisaAffiliation unknown1
Sitti NurhalizaAffiliation unknown1
Wiwik Andriyani Lestari NingsihAffiliation unknown1
Ismail PranataAffiliation unknown1
Muhammad Nur AidiAffiliation unknown1
Budi SusetyoAffiliation unknown1
Satria June AdwendiAffiliation unknown1
Aji Hamim WigenaAffiliation unknown1
Nindi PigithaAffiliation unknown1
Agus Mohamad SolehIPB University1
Indra Rivaldi SiregarAffiliation unknown1
Bagus SartonoIPB University1
Lukmanul HakimAffiliation unknown1
Rahma AnisaAffiliation unknown1
Mely AmeliaAffiliation unknown1

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Analisis Data Produk Domestik Regional Bruto Pulau Jawa Menggunakan Pendekatan Regresi Kuantil Spasial </dc:title>
	<dc:creator xml:lang="en">Usman, Lismayani</dc:creator>
	<dc:creator xml:lang="en">Saefuddin, Asep</dc:creator>
	<dc:creator xml:lang="en">Djuraidah, Anik</dc:creator>
	<dc:subject xml:lang="en">Gross Regional Domestic Product</dc:subject>
	<dc:subject xml:lang="en">Instrumental Variable Quantile Regression</dc:subject>
	<dc:subject xml:lang="en">Spatial Autoregressive Quantile Regression</dc:subject>
	<dc:subject xml:lang="en">Two Stage Quantile Regression</dc:subject>
	<dc:description xml:lang="en">Gross Regional Domestic Product (GRDP) often shows spatial patterns. In a spatial perspective, spatial effects consist of of spatial dependence and spatial heterogeneity. To address the problems, this study uses spatial autoregressive quantile regression/SARQR model. SARQR is a method that combines Spatial Autoregressive (SAR) modeling with quantile regression. There are two methods that can be used to estimate the parameters of the SARQR model, namely Two Stage Quantile Regression (2SQR) and Instrumental Variable Quantile Regression (IVQR). The simulation results showed that IVQR method is better than 2SQR method. IVQR provides a smaller value and variance of bias. Furthermore, IVQR method is applied to Java’s GRDP data on 2019. The results showed that the number of workers significantly influences Java’s GRDP. The highest quantile verification skill score (QVSS) value is 0.713 when τ =0.75. It means that in the 75% quantile modeling, the model can describe the GRDP diversity of 71.3%.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-08-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/27573</dc:identifier>
	<dc:identifier>10.20956/ejsa.v4i2.27573</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 2, Juli, 2023 : Estimasi; 153-164</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/27573/10207</dc:relation>
</oai_dc:dc>