Prakiraan Laju Inflasi Kota-Kota di Pulau Sulawesi: Pendekatan Model ARMA
Forecasting Inflation Rate of Citie in Sulawesi Island: ARMA Model Approach
Abstract
Penelitian ini bertujuan untuk memproyeksikan angka inflasi 6 Kota di Sulawesi menggunakan data bulanan dengan metode Box dan Jenkins. Data meliputi inflasi bulanan periode januari 2013 – juni 2023 mencakup 6 Kota di Sulawesi. Adapun tahapan metode peramalan meliputi uji stasioneritas data yang telah stasioner pada tingkat level, pemilihan ordo terbaik menghasilkan model ARMAberbeda pada masing-masing Kota. Hasil analisis estimasi inflasi 6 Kota di Sulawesi menunjukan tren berfluktuatif dengan proyeksi inflasi Kota Gorontalo tahun 2023 diperkirakan sebesar 2,90, inflasi Kota Kendari sebesar 4,98, inflasi Kota Makassar sebesar 3,55, inflasi Kota Manado sebesar 2,80, inflasi Kota Palu sebesar 3,17 dan inflasi Kota Mamuju sebesar 3,65. Analisis estimasi tidak mengandung unsur heteroskedastisitas sehingga tidak perlukan pengujian model ARCH/GARCH.
References
Asmarani, T. E. (2023). Peramalan Inflasi dengan Menggunakan Metode Arima: Studi di Indonesia. Journal on Education, 05(02).
Bollerslev, T. (2023). Reprint of: Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 234, 25–37. https://doi.org/10.1016/j.jeconom.2023.02.001
Fahrudin, R., & Sumitra, I. D. (2020). PERAMALAN INFLASI MENGGUNAKAN METODE SARIMA DAN SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: KOTA BANDUNG). Majalah Ilmiah UNIKOM, 17(2). https://doi.org/10.34010/miu.v17i2.3180
Ftiti, Z., & Jawadi, F. (2019). Forecasting Inflation Uncertainty in the United States and Euro Area. Computational Economics, 54(1). https://doi.org/10.1007/s10614-018-9794-9
Gam, T., Nainggolan, N., & Komalig, H. A. H. (2022). Analisis Volatilitas dan Peramalan Inflasi di Maluku Utara Menggunakan Model Generalized Autoregressive Conditional Heteroscedasticity (GARCH). Jurnal LPPM Bidang Sains Dan Teknologi, 7(2), 8–18.
GAUTAM, R. S., & KANOUJIYA, J. (2022). Inflation Targeting: An Application of ARIMA Modelling Using Forecasting of CPI and WPI”. Iconic Research and Engineering …, 5(11), 195–198. https://www.irejournals.com/formatedpaper/1703468.pdf
Hasnanda, S., & Ratna, R. (2020). The Generalized Autoregressive Conditional Heteroscedasticity Model Application on Inflation and Consumers Price Index in Aceh. Journal of Malikussaleh Public Economics, 3(1). https://doi.org/10.29103/jmpe.v3i1.3191
Hussain, M., Bashir, A., Wang, C., & Wang, Y. (2023). World uncertainty, natural resources, consumer prices, and financial development in high-income countries. Resources Policy, 81. https://doi.org/10.1016/j.resourpol.2023.103302
Khan, A., Khan, N., & Shafiq, M. (2021). The economic impact of COVID-19 from a global perspective. Contemporary Economics, 15(1). https://doi.org/10.5709/ce.1897-9254.436
Krukovets, D., & Verchenko, O. (2019). Short-Run Forecasting of Core Inflation in Ukraine: a Combined ARMA Approach. Visnyk of the National Bank of Ukraine, 248. https://doi.org/10.26531/vnbu2019.248.02
Malikova, D. (2023). INFLATION: THEORETICAL ASPECTS AND ANALYSIS OF PRICE CHANGES IN UZBEKISTAN. 11(2), 31–41.
Mankiw, N. G., Taylor, M. P., & Ashwin, A. (2016). “Business economics.” Cengage Learning.
Metcalfe, A. V., & Cowpertwait, P. S. P. (2009). Introductory Time Series with R. In Introductory Time Series with R. https://doi.org/10.1007/978-0-387-88698-5
Nyoni, T. (2018). Modeling and forecasting inflation in Kenya: Recent insights from ARIMA and GARCH analysis. Dinamorian Review, 5(6).
Parkin, M. (2018). Microeconomics?: Canada in the global environment. Pearson Canada.
Stonecash, R. E., Libich, J., Gans, J., King, S., Mankiw, N. G., & Byford, M. (2018). Principles of Macroeconomics Asia-Pacific Edition with Online Study (7th editio). https://books.google.co.id/books?hl=id&lr=&id=xkNMDwAAQBAJ&oi=fnd&pg=PR6&dq=Stonecash,+R.+E.,+Libich,+J.,+Gans,+J.,+King,+S.,+Mankiw,+N.+G.,+%26+Byford,+M.+(2017).+Principles+of+Macroeconomics+Asia-Pacific+Edition+with+Online+Study+Tool+S+12+Months&ots=df
Zhang, B., Chan, J. C. C., & Cross, J. L. (2020). Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts. International Journal of Forecasting, 36(4). https://doi.org/10.1016/j.ijforecast.2020.01.004
https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?end=2022&start=2012&view=chart






