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FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 2 (2023) · pp. 1183–1192

DOI
10.30598/barekengvol17iss2pp1183-1192
ISSN
2615-3017, 1978-7227
Published
2023-06-11
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city

Collaboration network of the authors

17 authors · 33 co-authorship links

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  • Universitas Muhammadiyah Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Rochdi WasonoAffiliation unknown2
Rahmad Putra GautamaAffiliation unknown2
Bunga AyuningrumAffiliation unknown1
Hilma Hanna Mahanna HaqqAffiliation unknown1
Suci Mega Puji LestariAffiliation unknown1
Laily HimmaturrohmahAffiliation unknown1
Indah Manfaati NurAffiliation unknown1
Nun Maulida Suci AyomiAffiliation unknown1
Yulia FitriAffiliation unknown1
mohammad ridwanAffiliation unknown1
Ina AlfidayantiAffiliation unknown1
Muhammad Sam’anUniversitas Muhammadiyah Semarang1
Wulan SariAffiliation unknown1
Fatkhurokhman FauziAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME</dc:title>
	<dc:creator>Arum, Prizka Rismawati</dc:creator>
	<dc:creator>Indriani, Anita Retno</dc:creator>
	<dc:creator>Haris, M Al</dc:creator>
	<dc:description xml:lang="en-US">Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-06-11</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8205</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss2pp1183-1192</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1183-1192</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss2year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8205/5967</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Prizka Rismawati Arum, Anita Retno Indriani, M Al Haris</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>