Generalized space-time autoregressive (GSTAR) modeling with seemingly unrelated regression (SUR) for forecasting inflation data in five cities on the island of Sumatra

• Main Authors: Suciati, Indah, Widiarti, Usman, Mustofa, Warsono, Wamiliana
• Format: Conference Proceeding
• Language: English
• Subjects: Cities; Forecasting; Parameter estimation; Parameter identification; Regression models; Time series
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0208198

The Covid-19 pandemic and Russian invasion of Ukraine made food and energy prices in the world soar. This price spike has triggered inflation in various countries, such as Indonesia. Currently, the most developed forecasting method is the time series. The development of a multivariate time series, apart from looking at the time element, also involves an element of location. One model that involves time and location is the Generalized Space Time Autoregressive (GSTAR) model. The GSTAR model is a development of the Space Time Autoregressive (STAR) model which assumes that the location element is heterogeneous. The purpose of this study was to obtain the GSTAR model using Seemingly Unrelated Regression (SUR) on inflation data in five cities on the island of Sumatra. The methods used in this study are, 1) perform descriptive analysis, 2) perform stationarity test, 3) identify the GSTAR model, 4) calculate the location weight matrix, and 5) calculate parameter estimates for the GSTAR model using the SUR method. In this study, it is found that the inflation model for five cities on the island of Sumatra at time t is correlated with inflation data at the previous time and is influenced by inflation data from other cities.