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VECTOR AUTOREGRESSIVE INTEGRATED (VARI) MENGGUNAKAN SOFTWARE R VECTOR AUTOREGRESSIVE INTEGRATED (VARI) USING SOFTWARE R Saputra, Andri; mirtawati, mirtawati
Baut Dan Manufaktur Vol 2 No 1 (2020): Jurnal Baut Dan Manufaktur Vol. 2 No. 1 Tahun 2020
Publisher : Fakultas Sains Dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (778.782 KB)

Abstract

Model Vector Autoregressive Integrated (VARI) merupakan perluasan dari model Autoregressive Integrated (ARI). Model VARI suatu model deret waktu multivariat yang dipengaruhi oleh variabel itu sendiri dan variabel lain pada periode sebelumnya dimana data tidak stasioner. Proses dalam menerapakn model VARI meliputi differencing, identifikasi, stasioneritas, estimasi parameter, uji diagnostik, dan peramalan. Pada penelitian ini, dengan asumsi galat berdistribusi normal, estimasi parameter model VARI dapat menggunakan metode Maximum Likelihood Estimation (MLE) dengan memaksimumkan fungsi ln likelihood. Data yang digunakan adalah nilai Impor dan eskpor Indonesia.
ANALISIS REGRESI DATA PANEL PADA FAKTOR-FAKTOR YANG MEMPENGARUHI KEMISKINAN DI INDONESIA TAHUN 2015 – 2019 Mirtawati, Mirtawati; Aulina, Nadiya
Kinerja Vol 4 No 1 (2021): Kinerja : Jurnal Ekonomi dan Bisnis
Publisher : Fakultas Ekonomi dan Bisnis Universitas Islam As-Syafi'iyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34005/kinerja.v4i1.1781

Abstract

High rate of poverty in Indonesia is shown by the large number of poor people. This condition means that the economic development is not still be able to increase the walfare of the people. First, we need to analyze which factors that might be able to significantly affect the rates of poverty. The goal of this research is to determine those factors in Indonesia from 2015-2019. Secondary data is used in this research along with Data Panel Regression Analysis that is consisted of time series data in range 2015-2019 and cross section data of 33 provinces in Indonesia that is processed by Eviews 9. The regression model is obtained from Ordinary Least Square estimation through fixed effect model approach using dummy variable to find out different intercept in each provinces which explaining different area. The result show that the economic growth is significantly and negatively affecting the poverty in Indonesia from 2015 to 2019. DKI Jakarta, Riau Islands, and East Kalimantan negatively affected to the factors of poverty while Papua, West Papua, and East Nusa Tenggara positively affected.