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Faiz AM Elfaki
Department of Mathematics, Statistics, and Physics, College of Arts and Sciences, Qatar University, Doha, 2713, Qatar
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Materials Science & Nanotechnology Physics

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Analysis Multivariate Time Series Using State Space Model for Forecasting Inflation in Some Sectors of Economy in Indonesia Edwin Russel; Wamiliana Wamiliana; Warsono; Nairobi; Mustofa Usman; Faiz AM Elfaki
Science and Technology Indonesia Vol. 8 No. 1 (2023): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2023.8.1.144-150

Abstract

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.
Co-Authors . Wamiliana Edwin Russel Mustofa Usman Nairobi Warsono Warsono