Peramalan Inflasi dengan Menggunakan Metode Arima: Studi di Indonesia
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Jan 26, 2023
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Abstract
The paper contains inflation forecasts using the Autoregressive Integrated Moving Average (ARIMA) method. The data used is Consumer Price Index data from January 2014 to March 2022. Using an optimistic scheme, inflation in 2023 is predicted to be 3.833%. The inflation projection is also within the national inflation projection range set by the government of 3% ± 1% or 2.0 – 4.0 percent in 2023.
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Asmarani, T. (2023). Peramalan Inflasi dengan Menggunakan Metode Arima: Studi di Indonesia. Journal on Education, 5(2), 4684-4692. Retrieved from https://jonedu.org/index.php/joe/article/view/1200
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References
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Mohamed, B., Abdulrahman, A., Yousef, A., Ahmed, A., Elshazali, A., & Abdellah, Y. (2018). Forecasting of Sudan Inflation Rates using ARIMA Model. International Journal of Economics and Financial Issues, 8(3), 17–22. http:www.econjournals.com
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Nyoni, T. (2018). Modeling and Forecasting Inflation in Kenya : Recent Insights. November.
Nyoni, T. (2019a). ARIMA modeling and forecasting of inflation in Egypt ( 1960-2017 ). Munich Personal RePEc Archive.
Nyoni, T. (2019b). Modeling and forecasting CPI in Tanzania. 92423.
Nyoni, T., & Mutongi, C. (2019). Modeling and forecasting inflation in Burundi using ARIMA models. Munich Personal RePEc Archive, 92444, 0–8. https://mpra.ub.uni-muenchen.de/93984/
Salisu, A. A., & Isah, K. O. (2018). Predicting US inflation: Evidence from a new approach. Economic Modelling, 71(December), 134–158. https://doi.org/10.1016/j.econmod.2017.12.008
Bank Indonesia. (2018). Laporan Perekonomian Indonesia 2018.
Bank Indonesia. (2020a). Indonesia Economic Report 2020 “Synergy to Build Optimism for Economic Recovery.” Bersinergi Membangun Optimisme Pemulihan Ekonomi, 112. https://www.bi.go.id/id/publikasi/laporan-tahunan/perekonomian/Documents/9_LPI2020.pdf
Bank Indonesia. (2020b). Laporan Perekonomian Indonesia 2019 “Sinergi, Transformasi, dan Inovasi Menuju Indonesia Maju.” 178. https://www.bi.go.id/id/publikasi/laporan-tahunan/perekonomian/Documents/9_LPI2019.pdf
Bank Indonesia. (2557). Laporan Perekonomian Indonesia 2021. 4(1), 88–100.
BPS. (2008). Statistics Indonesia. http://www.bps.go.id/tab_sub/view.php?kat=2&tabel=1&daftar=1&id_subyek=02¬ab=2
David, I. J. (2018). A predictive Autoregressive Integrated Moving Average (ARIMA) Model for forecasting inflation rates. Research Journal of Business and Economic Management, 1(1), 1–8. https://doi.org/10.31248/RJBEM2018.012
Mohamed, B., Abdulrahman, A., Yousef, A., Ahmed, A., Elshazali, A., & Abdellah, Y. (2018). Forecasting of Sudan Inflation Rates using ARIMA Model. International Journal of Economics and Financial Issues, 8(3), 17–22. http:www.econjournals.com
Nachrowi Djalal; Usman, H. (2006). Ekonometrika Pendekatan Populer dan Praktis untuk Analisis Ekonomi dan Keuangan (Vol. 5, Issue 1, pp. 87–88).
Niomy, T., & Nathaniel, S. P. (2019). Modeling rates of inflation in Nigeria : an application of ARMA , ARIMA and GARCH models. Munich Personal RePEc Archive, 91351, 1–29.
Nyoni, T. (2018). Modeling and Forecasting Inflation in Kenya : Recent Insights. November.
Nyoni, T. (2019a). ARIMA modeling and forecasting of inflation in Egypt ( 1960-2017 ). Munich Personal RePEc Archive.
Nyoni, T. (2019b). Modeling and forecasting CPI in Tanzania. 92423.
Nyoni, T., & Mutongi, C. (2019). Modeling and forecasting inflation in Burundi using ARIMA models. Munich Personal RePEc Archive, 92444, 0–8. https://mpra.ub.uni-muenchen.de/93984/
Salisu, A. A., & Isah, K. O. (2018). Predicting US inflation: Evidence from a new approach. Economic Modelling, 71(December), 134–158. https://doi.org/10.1016/j.econmod.2017.12.008