Pemodelan Efek Transmisi Inflasi Minyak Goreng di Jawa Barat, Jawa Tengah, dan Jawa Timur dengan Pendekatan BEKK GARCH

M. Hafidz Habibullah

Abstract

Konsumsi minyak goreng di Indonesia menunjukkan kecenderungan meningkat dan berfluktuasi setiap tahunnya. Selain faktor internal, volatilitas inflasi minyak goreng juga dapat dipengaruhi oleh spillover effect dari wilayah lain. Dengan mengasumsikan adanya heteroskedastisitas pada model rata-rata, model Baba, Engel, Kroner-generalized auto regressive conditional heteroscedasticity (BEKK-GARCH) cocok digunakan untuk menganalisis volatilitas spillover effect dari inflasi minyak goreng di Jawa Barat, Jawa Tengah, dan Jawa Timur. Efek transmisi berita, yang ditandai dengan transmisi dua arah, terjadi dari inflasi minyak goreng di Jawa Barat ke Jawa Timur dan dari Jawa Tengah ke Jawa Timur, sedangkan transmisi satu arah diamati dari Jawa Timur ke Jawa Tengah. Untuk spillover effect , yang ditandai dengan transmisi volatilitas dua arah, terjadi antara Jawa Barat dan Jawa Timur, serta dari Jawa Tengah ke Jawa Timur, sementara transmisi satu arah terjadi dari Jawa Tengah ke Jawa Barat. Oleh karena itu, pengendalian inflasi harus difokuskan pada wilayah dengan tingkat konsumsi tertinggi.

Cooking oil consumption in Indonesia shows a tendency to increase and fluctuate annually. Besides internal factors, the volatility of cooking oil inflation can also be influenced by spillover effects from other regions. Assuming heteroscedasticity in the average model, the Baba, Engel, Kroner-generalized auto regressive conditional heteroscedasticity (BEKK-GARCH) model is suitable for analyzing the volatility spillover effect of cooking oil inflation in West Java, Central Java, and East Java. The news transmission effect, marked by two-way transmission, occurs from cooking oil inflation in West Java to East Java and from Central Java to East Java, while one-way transmission is observed from East Java to Central Java. For the spillover effect, characterized by two-way volatility transmission, it takes place between West Java and East Java, as well as from Central Java to East Java, while one-way transmission occurs from Central Java to West Java. Consequently, controlling inflation should focus on regions with the highest consumption levels.

Kata kunci: Inflasi minyak goreng, spillover effect, BEKK-GARCH.

Keywords: Cooking oil inflation, spillover effect, BEKK-GARCH.

References

Khoiru Rizqy Rambe and Nunung Kusnadi, “Permintaan dan penawaran minyak goreng sawit Indonesia,” Forum Agribisnis, vol. 8, pp. 61–80, 2018. doi: https://doi.org/10.29244/fagb.8.1.61-80.

Chusnul Islamiyati Jusuf, Ria Indriani, and Echan Adam, “Faktor sosial ekonomi masyarakat dan pola konsumsi minyak goreng di Kelurahan Pulubala Kota Gorontalo,” Jurnal Ilmiah Membangun Desa dan Pertanian, vol. 8, no. 6, pp. 223–233, 2023. doi: https://doi.org/10.37149/jimdp.v8i6.549

Agnes Z. Yonathan, “Konsumsi minyak goreng di Indonesia cenderung stabil,” Apr. 2024, Accessed: Jan. 25, 2025. [Online]. Available: https://data.goodstats.id/statistic/konsumsi-minyak-goreng-di-indonesia-cenderung-stabil-gtJOj

Kementerian Pertanian, “Buletin Konsumsi Pangan 2023,” Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal Kementerian Pertanian, 2023.

Rizky Ramadini Febrina, “Kebijakan pemerintah dalam mengatasi kenaikan harga minyak goreng,” Trade Poolicy Journal, vol. 1, no. 1, 2022.

Febianto, Sri Gunawan, and Samsuri Tarmadja, “Kajian faktor-faktor terjadinya losses di perkebunan kelapa sawit pada togpografi datar, rendahan, dan berbukit,” Agroforetech, vol. 1, no. 2, 2023. [Online]. Available: https://jurnal.kemendag.go.id/index.php/TPJ/article/view/758/385

Kementerian Koordinator Bidang Perekonomian Republik Indonesia, “Tingkatkan produktivitas sawit rakyat, pemerintah dorong sejumlah kebijakan strategis.” Accessed: Jan. 25, 2025. [Online]. Available: https://www.ekon.go.id/publikasi/detail/5648/tingkatkan-produktivitas-sawit-rakyat-pemerintah-dorong-sejumlah-kebijakan-strategis

J. E. Hanke and D. Wichern, Business Forecasting, 9th ed. Pearson Education, 2014.

K. Nugroho, “Model analisis prediksi menggunakan metode fuzzy time series,” Jurnal Ilmiah Informasi Komputer Akuntansi dan Manajemen, vol. 12, no. 1, pp. 46–50, 2016. doi: https://doi.org/10.53845/infokam.v12i1.91.

R. N. T. Wahyuni and N. Nasrudin, “Spillover effect inflasi daging sapi antar kota: aplikasi metode BEKK-GARCH untuk Jakarta, Salatiga, dan Surabaya,” Medstat, vol. 13, no. 1, pp. 80–91, Jun. 2020, doi: https://doi.org/10.14710/medstat.13.1.80-91

L. Barbaglia, C. Croux, and I. Wilms, “Volatility spillovers in commodity markets: A large t-vector autoregressive approach,” Energy Economics, vol. 85, p. 104555, Jan. 2020. doi: https://doi.org/10.1016/j.eneco.2019.104555.

H. Xue, Y. Du, Y. Gao, and W.-H. Su, “Spatial price transmission and dynamic volatility spillovers in the global grain markets: A TVP-VAR-connectedness approach,” Foods, vol. 13, no. 20, p. 3317, Oct. 2024, doi: https://doi.org/10.3390/foods13203317.

M. P. Ardianto, “Pemodelan generalized space time autoregressive (gstar) pada tiga periode waktu (studi kasus inflasi di lima kota besar di Pulau Jawa),” Universitas Brawijaya, Malang, 2014.

S. S. Wibowo and B. Hermanto, “Perbandingan metode GARCH BEKK dan matriks varian-kovarian untuk penaksiran volatilitas mata uang dan VAR,” Manajemen Usahawan Indonesia, vol. 35, no. 12, pp. 29–38, 2006. [Online] . Available : https://www.academia.edu/10107725/Perbandingan_Metode_GARCH_BEKK_dan_Metode_Matriks_Varian_Kovarian_dalam_Rangka_Penaksiran_Volatilitas_Mata_Uang_dan_Value_at_Risk

V. Dehbashi, T. Mohammadi, J. Bahrami, and A. Shakeri, “Comparing the volatility spillovers among financial markets in Iran pre and post JCPOA: A VAR-BEKK-GARCH approach,” IER, no. Online First, Jul. 2020. doi: https://doi.org/10.22059/ier.2020.77210.

Badan Pusat Statistik, “Statistik Kelapa Sawit Indonesia,” Badan Pusat Statistik, Jakarta, 2023.

M. Hanri, N. K. Sholihah, and F. R. Moeis, “Labor Market Brief,” Lembaga Penyelidikan Ekonomi Masyarakat, Depok, 2022.

T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, Apr. 1986. doi: https://doi.org/10.1016/0304-4076(86)90063-1.

G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control, Fifth edition. in Wiley series in probability and statistics. Hoboken, New Jersey: Wiley, 2016.

A. Silvennoinen and T. Terasvirta, “Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model,” Journal of Financial Econometrics, vol. 7, no. 4, pp. 373–411, Oct. 2009. doi: https://doi.org/10.1093/jjfinec/nbp013.

D. De Almeida, L. K. Hotta, and E. Ruiz, “MGARCH models: Trade-off between feasibility and flexibility,” International Journal of Forecasting, vol. 34, no. 1, pp. 45–63, Jan. 2018. doi: https://doi.org/10.1016/j.ijforecast.2017.08.003.

B. Juanda and Junaidi, Ekonometrika Deret Waktu: Teori dan Aplikasi. Bogor: IPB Press, 2012.

K. D. Melas, A. Faitatzoglou, N. A. Michail, and A. Artemiou, “Volatility spillovers among the major commodities: A Review,” JRFM, vol. 17, no. 8, p. 365, Aug. 2024. doi: https://doi.org/10.3390/jrfm17080365.

DOI: https://doi.org/10.13057/ijas.v9i1.100076

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