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Estimation of Dynamic Panel Data Regression Parameters Using Generalized Methods of Moment

Bahasa Indonesia

Jurnal Matematika, Statistika dan Komputasi · Vol. 18 No. 3 (2022) · pp. 484–491

DOI
10.20956/j.v18i3.20574
ISSN
2614-8811, 1858-1382
Published
2022-05-15
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

Panel data is a combination of cross section and time series. There are two panel data models, namely static and dynamic panel data. Because seeing the advantages of the dynamic panel data model which is able to overcome endogeneity problems related to the use of the dependent variable lag where in the static panel data model the use of the dependent variable lag causes the estimation results to be biased and inconsistent, so the author examines the dynamic panel data regression model. In the dynamic data model there is a lag of the dependent variable, this variable is correlated with error. Thus, estimation using OLS will result in a biased and inconsistent estimator. To overcome this, the dynamic panel data model can be estimated using the GMM Blundell-Bond approach. Based on the discussion, the parameter estimation formula for dynamic panel data regression using the Blundell-Bond GMM approach is as follows:

Bahasa Indonesia

Data panel merupakan gabungan dari cross section dan time series. Terdapat dua model data panel yaitu data panel statis dan dinamis. Karena melihat keunggulan model data panel dinamis yang sanggup mengatasi masalah endogenitas terkait penggunaan lag variabel dependen dimana pada model data panel statis penggunaan lag variabel dependen menyebabkan hasil estimasi menjadi bias dan tidak konsisten sehingga penulis meneliti tentang model regresi data panel dinamis. Pada model data dinamis terdapat lag dari variabel dependen, variabel ini berkorelasi dengan error. Maka, estimasi menggunakan OLS akan menghasilkan estimator yang bias dan tidak konsisten. Untuk mengatasi hal tersebut, model data panel dinamis dapat diestimasi dengan pendekatan GMM Blundell-Bond. Berdasarkan pembahasan diperoleh rumus estimasi parameter regresi data panel dinamis dengan pendekatan GMM Blundell-Bond adalah sebagai berikut:

Keywords

Collaboration network of the authors

35 authors · 113 co-authorship links

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

  • Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi
  • Institut Agama Islam Negeri Kendari
  • Jenderal Soedirman University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Erna Tri HerdianiAffiliation unknown3
Andi Isna YunitaAffiliation unknown3
Siswanto SiswantoAffiliation unknown2
Raupong RaupongAffiliation unknown2
Vieri KoerniawanAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Novilia JaoAffiliation unknown1
Riska RasyidAffiliation unknown1
Andi Kresna JayaAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
- SiswantoAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Ayu Pratika PutriAffiliation unknown1
Annurial Fitrayah TaufiqAffiliation unknown1
Ahmad Maulana SyarifAffiliation unknown1
Nurmimie KharinaAffiliation unknown1
Samsir Aditya AniaAffiliation unknown1
Herlina RasyidUniversitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi1
Nur Aliah RamadhaniAffiliation unknown1
Nadia Nazwadiah CaesarAffiliation unknown1
Zaraswati Dwyana ZainuddinAffiliation unknown1
Nurfadilah NurfadilahInstitut Agama Islam Negeri Kendari1
Andi M Alfin BasoAffiliation unknown1
Muhammad Ridzky DavalaAffiliation unknown1
Agung Muhammad TakdirAffiliation unknown1
Nasrah SirajangAffiliation unknown1
YULIANA.DEWI PutriAffiliation unknown1
Amran AmranJenderal Soedirman University1
Hanifah LainunAffiliation unknown1
Puji Puspa SariAffiliation unknown1
Aidawayati RangkutiAffiliation unknown1
Aprizal ReskyAffiliation 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/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Estimation of Dynamic Panel Data Regression Parameters Using Generalized Methods of Moment </dc:title>
	<dc:title xml:lang="id">Bahasa Indonesia</dc:title>
	<dc:creator xml:lang="en">Ahmad, Nur Aminah</dc:creator>
	<dc:creator xml:lang="en">Tinungki, Georgina M. </dc:creator>
	<dc:creator xml:lang="en">Sunusi, Nurtiti </dc:creator>
	<dc:subject xml:lang="en">Panel Data Regression</dc:subject>
	<dc:subject xml:lang="en">Dynamic Panel Data Regression</dc:subject>
	<dc:subject xml:lang="en">Generalized Methods of Moment</dc:subject>
	<dc:description xml:lang="en">Panel data is a combination of cross section and time series. There are two panel data models, namely static and dynamic panel data. Because seeing the advantages of the dynamic panel data model which is able to overcome endogeneity problems related to the use of the dependent variable lag where in the static panel data model the use of the dependent variable lag causes the estimation results to be biased and inconsistent, so the author examines the dynamic panel data regression model. In the dynamic data model there is a lag of the dependent variable, this variable is correlated with error. Thus, estimation using OLS will result in a biased and inconsistent estimator. To overcome this, the dynamic panel data model can be estimated using the GMM Blundell-Bond approach. Based on the discussion, the parameter estimation formula for dynamic panel data regression using the Blundell-Bond GMM approach is as follows:
 
 </dc:description>
	<dc:description xml:lang="id">Data panel merupakan gabungan dari cross section dan time series. Terdapat dua model data panel yaitu data panel statis dan dinamis. Karena melihat keunggulan model data panel dinamis yang sanggup mengatasi masalah endogenitas terkait penggunaan lag variabel dependen dimana pada model data panel statis penggunaan lag variabel dependen menyebabkan hasil estimasi menjadi bias dan tidak konsisten sehingga penulis meneliti tentang model regresi data panel dinamis. Pada model data dinamis terdapat lag dari variabel dependen, variabel ini berkorelasi dengan error. Maka, estimasi menggunakan OLS akan menghasilkan estimator yang bias dan tidak konsisten. Untuk mengatasi hal tersebut, model data panel dinamis dapat diestimasi dengan pendekatan GMM Blundell-Bond. Berdasarkan pembahasan diperoleh rumus estimasi parameter regresi data panel dinamis dengan pendekatan GMM Blundell-Bond adalah sebagai berikut:
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	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2022-05-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/20574</dc:identifier>
	<dc:identifier>10.20956/j.v18i3.20574</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 18 No. 3 (2022): MAY, 2022; 484-491</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/20574/8158</dc:relation>
	<dc:relation>10.20956/j.v18i3.20574.g8158</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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