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Abstract

The macroeconomic indicator used to measure a country’s economic balance is inflation. The increase in the price of goods and services causes an increase in inflation, which impacts the decrease in the value of money so that people’s purchasing power for goods and services will decrease and result in slow economic growth. One way to determine future inflation is by forecasting. The Generalized Space-Time Autoregressive (GSTAR) model is a time series model involving time and location. This study aims to predict future inflation using the GSTAR model, which uses differencing without uniform location weights, inverse distance, and normalized cross-correlation. The results showed that the models obtained were the GSTAR (2,1) and GSTAR (5,1)I(1) models. The best model to predict inflation is the GSTAR (5,1)I(1) model with the normalized cross-correlation weight, which had Root Mean Square Error (RMSE) value of 0.5743, which was smaller than the GSTAR (2,1) model.

Keywords

Inflation Time series GSTAR Forecasting

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How to Cite
Hestuningtias, F., & Kurniawan, M. H. S. (2023). The Implementation of the Generalized Space-Time Autoregressive (GSTAR) Model for Inflation Prediction. Enthusiastic : International Journal of Applied Statistics and Data Science, 3(2), 176–188. https://doi.org/10.20885/enthusiastic.vol3.iss2.art5
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References

  1. S. Suseno and S. Astiyah, Inflasi. Jakarta: Pusat Pendidikan dan Studi Kebanksentralan (PPSK) Bank Indonesia, 2009.
  2. P.A. Daniel, “Analisis Pengaruh Inflasi terhadap Laju Pertumbuhan Ekonomi di Kota Jambi,” Jurnal of Economics and Business, vol. 2, no. 1, pp. 131–136, Mar. 2018, doi: 10.33087/ekonomis.v2i1.37.
  3. A. Kabukçuoğlu and E. Martínez-García, “Inflation as a Global Phenomenon—Some Implications for Inflation Modeling and Forecasting,” Journal of Economic Dynamics and Control, vol. 87, pp. 46–73, Feb. 2018, doi: 10.1016/j.jedc.2017.11.006.
  4. Badan Pusat Statistik Jambi, Perkembangan Indeks Harga Konsumen Provinsi Jambi Juli 2022. Jambi, Indonesia, 2022.
  5. Badan Pusat Statistik Sumatera Barat, Perkembangan Indeks Harga Konsumen Sumatra Barat Juli 2022. Sumatra Barat, Indonesia, 2022.
  6. Badan Pusat Statistik Kepulauan Bangka Belitung, Perkembangan Indeks Harga Konsumen Provinsi Kepulauan Bangka Belitung. Bangka Belitung, Indonesia, 2022.
  7. L. Faizah and S. Setiawan, “Pemodelan Inflasi di Kota Semarang, Yogyakarta, dan Surakarta dengan pendekatan GSTAR,” Jurnal Sains Dan Seni Pomits, vol. 2, no. 2, pp. 317–322, Apr. 2013, doi: 10.12962/j23373520.v2i2.4866.
  8. N. Nur’Eni, D. Lusiyanti, and I. Gunawan, “Identifikasi Model Generalized Space-Time Autoregressive (Gstar) untuk Nilai Inflasi di Pulau Sulawesi,” Jurnal Ilmiah Matematika dan Terapan, vol. 18, no. 1, pp. 75–83, Jun. 2021, doi: 10.22487/2540766X.2021.v18.i1.15522.
  9. G. Zhao, M. Xue, and L. Cheng, “A New Hybrid Model for Multi-Step WTI Futures Price Forecasting Based on Self-Attention Mechanism and Spatial–Temporal Graph Neural Network,” Resources Policy, vol. 85, pp. 1–18, Aug. 2023, doi: 10.1016/j.resourpol.2023.103956.
  10. D.S. Wutsqa, Suhartono, and B. Sutijo, “Generalized Space-Time Autoregressive Modeling,” in Proceedings of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA2010), 2010, pp. 752–761.
  11. M.I.T. Mario, Kartiko, and R.D. Bekti, “Pemodelan Generalized Space Time Autoregressive (GSTAR) untuk Peramalan Tingkat Inflasi di Pulau Jawa,” Jurnal Statistika Industri dan Komputasi, vol. 6, no. 2, pp. 171–184, Jul. 2021.
  12. N.M. Huda and N. Imro’ah, “Determination of the Best Weight Matrix for the Generalized Space Time Autoregressive (GSTAR) Model in the COVID-19 Case on Java Island, Indonesia,” Spatial Statistics, vol. 54, p. 100734, Apr. 2023, doi: 10.1016/j.spasta.2023.100734.
  13. U.S. Pasaribu, U. Mukhaiyar, N.M. Huda, K.N. Sari, and S.W. Indratno, “Modelling COVID-19 Growth Cases of Provinces in Java Island by Modified Spatial Weight Matrix GSTAR Through Railroad Passenger’s Mobility,” Heliyon, vol. 7, no. 2, Feb. 2021, doi: 10.1016/j.heliyon.2021.e06025.
  14. A.N. Islamiyah, W. Rahayu, and E.D. Wiraningsih, “Pemodelan Generalized Space Time Autoregressive (GSTAR) dan Penerapannya pada Penderita TB Paru (BTA+) di DKI Jakarta,” Jurnal Statistika dan Aplikasinya, vol. 2, no. 2, pp. 36–48, Dec. 2018, doi: 10.21009/JSA.02205.
  15. V.P. Balqis, E. Kurniati, and O. Rohaeni, “Model Peramalan Data Inflasi dengan Metode Generalized Space Time Autoregressive (Gstar) pada Tiga Kota di Jawa Barat,” in Prosiding Matematika, 2020, pp. 43–50.
  16. C.A. Widyastuti, A. Hoyyi, and R. Rahmawati, “Peramalan Pasang Surut Air Laut di Pulau Jawa Menggunakan Model Generalized Space Time Autoregressive (GSTAR),” Jurnal Gaussian, vol. 5, no. 4, pp. 623–632, Nov. 2016, doi: 10.14710/j.gauss.5.4.623-632.
  17. E.Z. Chini, “Forecasting Dynamically Asymmetric Fluctuations of the U.S. Business Cycle,” International Journal of Forecasting, vol. 34, no. 4, pp. 711–732, Oct.–Dec. 2018, doi: 10.1016/j.ijforecast.2018.05.003.
  18. H.D. Karlina, R. Cahyandari, and A.S. Awalluddin, “Aplikasi Model Generalized Space Time Autoregressive (GSTAR) pada Data Jumlah TKI Jawa Barat dengan Pemilihan Lokasi Berdasarkan Klaster DBSCAN,” Jurnal Matematika Integratif, vol. 10, no. 1, pp. 37–48, Apr. 2014, doi: 0.24198/jmi.v10.n1.10183.37-48.
  19. D. Anggraeni, A. Prahutama, and S. Andari, “Aplikasi Generalized Space Time Autoregressive (GSTAR) Pada Pemodelan Volume Kendaraan Masuk Tol Semarang,” Jurnal Media Statistika, vol. 6, no. 2, pp. 71–80, Dec. 2013, doi: 10.14710/medstat.6.2.61-70.
  20. F.N. Aryani, S.S. Handajani, and E. Zukhronah, “Penerapan Model Generalized Space Time Autoregressive (GSTAR) pada Data Nilai Tukar Petani 3 Provinsi di Pulau Sumatra,” in Prosiding Seminar Nasional Pendidikan Matematika Universitas Pekalongan, 2020, pp. 209–220.
  21. E. Siswanto, H. Yasin, and Sudarno, “Pemodelan Generalized Space Time Autoregressive (GSTAR) Seasonal pada Data Curah Hujan Empat Kabupaten di Provinsi Jawa Tengah,” Jurnal Gaussian, vol. 8, no. 4, pp. 418–427, Nov. 2019, doi: 0.14710/j.gauss.8.4.418-427.