Pemodelan dan Peramalan Data Ekspor Sektor Pertanian Menggunakan Model Vector Autoregressive (VAR)
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https://doi.org/10.32665/james.v6i1.1030Keywords:
Vector Autoregressive (VAR), Granger Causality, Peramalan, ForecastingAbstract
Model Vector Autoregressive (VAR) merupakan salah satu pemodelan dalam statistika yang dapat digunakan untuk pemodelan data multivariat time series yang biasa ditemukan dalam bidang keuangan, manajemen, bisnis dan ekonomi. Model VAR menganalisis data time series secara simultan untuk mendapatkan kesimpulan yang tepat dan dapat menjelaskan perilaku hubungan antar variabel endogeneous maupun antar variabel endegeneous dan eksogeneous secara dinamis. Selain itu model VAR juga dapat menjelaskan mengenai hubungan antar variabel selain hanya dipengaruhi oleh faktor dirinya sendiri dari waktu ke waktu dengan menggunakan Granger Causality. Pada studi ini, kami akan mendiskusikan dan menentukan model terbaik yang dapat mendeskripsikan hubungan antar tiga vektor data timeseries yaitu data nilai ekspor komoditas pertanian dengan variabel nilai ekspor biji kopi, biji coklat dan tembakau Indonesia di mana data merupakan data bulanan dari tahun 2007-2018. Beberapa model diterapkan pada data seperti model VAR(1), VAR(2) ,VAR(3), VAR(4) dan VAR(5) . Hasilnya, model VAR(2) terpilih sebagai model terbaik dengan kriteria pemilihan model AICC, SBC, AIC dan HQC. Perilaku secara dinamis dari ketiga variabel nilai ekspor biji kopi, biji coklat dan tembakau Indonesia dijelaskan oleh Granger Causality. Selanjutnya, forecasting dari data ini dilakukan selama 10 bulan ke depan di mana model VAR(2) yang terpilih sebagai model terbaik hanya cocok digunakan untuk forecasting dalam waktu yang singkat.
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