Analysis Multivariate Time Series Using State Space Model for Forecasting Inflation in Some Sectors of Economy in Indonesia

Edwin Russel (Department of Management, Faculty of Economics & Business, Universitas Lampung, Bandar Lampung, 35141, Indonesia)
Wamiliana Wamiliana (Department of Mathematics, Faculty of Mathematics and Sciences, Universitas Lampung, Bandar Lampung, 35141, Indonesia)
Warsono (Department of Mathematics, Faculty of Mathematics and Sciences, Universitas Lampung, Bandar Lampung, 35141, Indonesia)
Nairobi (Department of Development Economics, Faculty of Economics & Business, Universitas Lampung, Bandar Lampung, 35141, Indonesia)
Mustofa Usman (Department of Mathematics, Faculty of Mathematics and Sciences, Universitas Lampung, Bandar Lampung, 35141, Indonesia)
Faiz AM Elfaki (Department of Mathematics, Statistics, and Physics, College of Arts and Sciences, Qatar University, Doha, 2713, Qatar)

Science and Technology Indonesia
Vol. 8 No. 1 (2023): January

Many analytical methods can be utilized for multivariate time series modeling. One of the analytical models for modeling time series data with multiple variables is the State Space Model. The data to be analyzed in this study is inflation data from expenditure groups such as processed foods, beverages, cigarettes, and tobacco; and housing inflation for water, electricity, gas, and fuel from January 2001 to December 2021. The aim is to determine the best State Space Model that fits the data for forecasting. In this study, the State Space method will be utilized further with multivariate time series data and represent State Space in Vector Autoregressive (VAR) to determine the relationship between groups of observed variables.