Hendriarto, Dion Haffiz (2023) Pemodelan Gross Domestic Product (GDP) Per Kapita Negara ASEAN-China Dengan Regresi Data Panel Dinamis Pendekatan Arrelano-Bond Generalized Method Of Moments. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Gross Domestic Product (GDP) per kapita merupakan salah satu indikator untuk membandingkan pertumbuhan ekonomi dan kesejahteraan penduduk antar negara. Hal ini didasari karena GDP per kapita mengukur standar kehidupan, yaitu pendapatan rata-rata individu di satu negara. Selain itu, GDP per kapita juga memungkinkan untuk melakukan perbandingan langsung antar negara-negara dengan ukuran populasi yang berbeda. GDP Perkapita dapat digunakan untuk pendapatan rata-rata tiap daerah dan dijadikan sebagai evaluasi standar hidup dan kualitas hidup penduduk. ASEAN-China terdiri dari sepuluh negara dengan karakteristiknya yang berbeda-beda sehingga diperlukan analisis faktor apa saja yang berpengaruh signifikan terhadap nilai GDP Perkapita. Penelitian ini menggunakan metode regresi data panel dinamis dengan pendekatan Arrelano-Bond Generalized Method of Moment (AB-GMM) yang terdiri dari sepuluh negara selama periode 2012-2021. Model dinamis digunakan karena variabel-variabel ekonomi umumnya bersifat dinamis, atau dipengaruhi periode sebelumnya. Selain itu, model dinamis dapat menentukan efek jangka pendek maupun jangka panjang dari model yang terbentuk. Hasil analisis menunjukkan bahwa variabel yang berpengaruh signifikan terhafap GDP perkapita adalah nilai tukar, dan konsumsi rumah tangga dengan koefisien tanda yang sesuai dengan teori ekonomi dengan nilai kebaikan model yang didapat sebesar 99,66%. Nilai tukar dan konsumsi rumah tangga masing-masing memiliki efek jangka pendek terhadap GDP Perkapita sebesar -0,270 dan 0,627. Sedangkan dalam efek jangka panjang, nilai tukar dan konsumsi rumah tangga memberikan efek jangka panjang sebesar -0,397 dan 0,922.
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Gross Domestic Product (GDP) per capita is an indicator to compare economic growth and population welfare between countries. This increases because GDP per capita measures the standard of living, i.e. the average income of individuals in a country. In addition, GDP per capita also allows for direct comparisons between countries with different population sizes. Per capita GDP can be used for average income per region and used as an evaluation of the standard of living and quality of life of the population. ASEAN-China consists of ten countries with different characteristics, so it is necessary to analyze which factors have a significant effect on the value of GDP per capita. This study uses the dynamic panel data regression method with the Arrelano-Bond Generalized Method of Moment (AB-GMM) approach which consists of ten countries during the 2012-2021 period. The dynamic model is used because economic variables are generally dynamic, or influenced by the previous period. In addition, dynamic models can determine the short-term and long-term effects of the model formed. The results of the analysis show that the variables that have a significant effect on GDP per capita are the exchange rate and household consumption with a sign coefficient that is in accordance with economic theory with the good model obtained by 99.66%. The exchange rate and household consumption each have a short-term effect on GDP per capita of -0,270 and 0,627. Meanwhile, in the long term, the exchange rate and household consumption have a long-term effect of -0,397 and 0,922.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Arrelano-Bond, ASEAN-China, GDP Perkapita, Regresi Data Panel Dinamis, GDP Percapita, Dynamic Panel Data Regression |
| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics H Social Sciences > HA Statistics > HA31.3 Regression. Correlation |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Dion Haffiz Hendriarto |
| Date Deposited: | 18 Sep 2023 01:38 |
| Last Modified: | 18 Sep 2023 01:38 |
| URI: | http://repository.its.ac.id/id/eprint/104374 |
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