Application of GSTARI (1,1,1) Model for Forecasting the Consumer Price Index (CPI) in Three Cities in Central Java

Noverlina Putri Permatasari; Husnul Chotimah; Pandu Permana; Wenny Srimeinda Tarigan; Toni Toharudin; Budi Nurani Ruchjana

doi:10.31764/jtam.v6i1.6114

Application of GSTARI (1,1,1) Model for Forecasting the Consumer Price Index (CPI) in Three Cities in Central Java

Noverlina Putri Permatasari, Husnul Chotimah, Pandu Permana, Wenny Srimeinda Tarigan, Toni Toharudin, Budi Nurani Ruchjana

Abstract

Economic development is affected by several factors, one of which is the inflation rate. One indicator used to measure the inflation rate is Consumer Price Index (CPI). The CPI data is recorded simultaneously at several locations over time, produces space-time data. In Central Java Province, CPI is calculated in six regency/cities, so the CPI is affected by the time and other locations named space-time effect. The forecasting methods involve space and time effect simultaneously is GSTAR. This study used the GSTAR model to forecasting the CPI in 3 cities in Central Java, assuming that autoregressive and space-time parameters differ for each location. This study aims to obtain the best GSTAR model to forecast the CPI in three cities in Central Java by using the IDW and NCC weighting. The results indicated that the best GSTAR model for forecast the CPI in three cities (Surakarta, Semarang, and Tegal) was the GSTARI (1,1,1) model. The GSTARI (1,1,1) model fulfils the assumption of homoscedasticity, white noise, and multivariate normal. The MAPE values obtained using the IDW and NCC weighting are 0.2922% and 0.2914%, respectively. From these results, it can be concluded that the best GSTARI (1,1,1) model to forecast the CPI data in three cities in Central Java is NCC weights, as they have a minimum MAPE value . The results of this research can be used as consideration for the government in making economic policies at the present and in the future.

Keywords

Space time; Autoregressive; GSTAR Model; Invers distance; Cross correlation

Full Text:

DOWNLOAD [PDF]

References

Ahmar, A. S., Daengs, A., Listyorini, T., Sugianto, C. A., Yuniningsih, Y., Rahim, R., & Kurniasih, N. (2018). Implementation of the ARIMA ( p , d , q ) Method to Forecasting CPI Data Using Forecast Package in R Software. Journal of Physics : Conference Series, 1028. https://doi.org/10.1088/1742-6596/1028/1/012189

Ardiansyah, H. (2017). Pengaruh Inflasi Terhadap Pertumbuhan Ekonomi di Indonesia. Jurnal Pendidikan Ekonomi, 5(3). https://doi.org/10.26740/jupe.v5n3.p%25p

Bonar, H., Ruchjana, B. N., & Darmawan, G. (2017). Development of generalized space time autoregressive integrated with ARCH error (GSTARI - ARCH) model based on consumer price index phenomenon at several cities in North Sumatera province. AIP Conference Proceedings, 1827. https://doi.org/10.1063/1.4979425

Borovkova, S., Lopuhaä, H. P., & Ruchjana, B. N. (2008). Consistency and asymptotic normality of least squares estimators in generalized STAR models. Statistica Neerlandica, 62(4), 482–508. https://doi.org/10.1111/j.1467-9574.2008.00391.x

Cheng, T., Haworth, J., & Wang, J. (2011). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14(2), 389–413. https://doi.org/10.1007/s10109-011-0149-5

Cheng, T., Wang, J., Haworth, J., Heydecker, B., & Chow, A. (2014). A Dynamic Spatial Weight Matrix and Localized Space – Time Autoregressive Integrated Moving Average for Network Modeling. Geographical Analysis, 46, 75–97. https://doi.org/10.1111/gean.12026

Deng, M., Yang, W., & Liu, Q. (2016). A divide-and-conquer method for space – time series prediction. Journal of Geographical Systems, 19, 1–19. https://doi.org/10.1007/s10109-016-0241-y

Fahlevi, R., Ernayani, R., Lestari, W., Hubur, A. A., & Wahyudi, A. (2020). A Brief Review on the Theory of Inflation. Journal of Critical Reviews, 7(08), 2069–2076.

Gospodinov, N., & Ng, S. (2013). Commodity prices, convenience yields, and inflation. The Review of Economics and Statistics, 95(1), 206–219.

Harini, S., & Nuronia, N. (2020). Determination of consumer price index with generalized space-time autoregressive. IOP Conference Series : Earth and Enviromental Science, 456, 012076. https://doi.org/10.1088/1755-1315/456/1/012076

Ireland, P. N. (2020). Independence and accountability via inflation targeting. Cato Journal, 40(2), 269–283. https://doi.org/10.36009/CJ.40.2.2.

Ji, S., Dong, J., Wang, Y., & Liu, Y. (2019). Research on CPI Prediction based on Space-Time Model. 2019 6th International Conference on Dependable Systems and Their Applications (DSA), 377–382. https://doi.org/10.1109/DSA.2019.00058

Kharimah, F., Usman, M., Widiarti, & Elfaki, F. (2015). Time Series Modeling and Forecasting of the Consumer Price Index Bandar Lampung. Science International, 27(5), 4619–4624.

Mukhaiyar, U., & Pasaribu, U. S. (2012). A New Procedure for Generalized STAR Modeling using IAcM Approach. ITB Journal Science, 44(2), 179–192. https://doi.org/10.5614/itbj.sci.2012.44.2.7

Pfeifer, P. E., & Deutrch, S. J. (1980). A Three-Stage Iterative Procedure for Space-Time Modelling Phillip. Technometrics, 22(1), 35–47. https://doi.org/10.1080/00401706.1980.10486099

Rathod, S., Gurung, B., Singh, K. N., & Ray, M. (2018). An improved Space-Time Autoregressive Moving Average ( STARMA ) model for Modelling and Forecasting of Spatio-Temporal time-series data. Journal of the Indian Society of Agricultural Statistics, 72(3).

Ruchjana, B. N., Borovkova, S. A., & Lopuhaa, H. P. (2012). Least Squares Estimation of Generalized Space Time AutoRegressive ( GSTAR ) Model and Its Properties. AIP Conference Proceedings, 1450, 61–64. https://doi.org/10.1063/1.4724118

Setiawan, Suhartono, & Prastuti, M. (2016). S-GSTAR-SUR Model for Seasonal Spatio Temporal Data Forecasting. Malaysian Journal of Mathematical Sciences, 10, 53–65.

Ulke, V., Sahin, A., & Subasi, A. (2016). A comparison of time series and machine learning models for inflation forecasting : empirical evidence from the USA. Neural Computing and Applications, 30, 1519–1527. https://doi.org/10.1007/s00521-016-2766-x

Wei, W. (2019). Multivariate Time Series Analysis and Applications. Wiley.

Yaziz, M., Mohd, B., & Mohamed, Z. Bin. (2017). Unit Roots and Co-Integration Tests : the Effects of Consumer Price Index ( CPI ) on Non-Performing Loans ( NPL ) in the Banking Sector in Malaysia. Journal of Advanced Statistics, 2(1), 16–25. https://dx.doi.org/10.22606/jas.2017.21003

Zou, J., Zhu, J., Member, S., Xie, P., & Xuan, P. (2018). A STARMA Model for Wind Power Space-Time Series. 2018 IEEE Power & Energy Society General Meeting (PESGM), 51379159, 1–5. https://doi.org/10.1109/PESGM.2018.8585919

DOI: https://doi.org/10.31764/jtam.v6i1.6114