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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Aji Hamim Wigena
Departement of Statistics, Faculty of Mathematics and Natural Sciences, IPB University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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THE PROMINENCE OF VECTOR AUTOREGRESSIVE MODEL IN MULTIVARIATE TIME SERIES FORECASTING MODELS WITH STATIONARY PROBLEMS Embay Rohaeti; I Made Sumertajaya; Aji Hamim Wigena; Kusman Sadik
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.398 KB) | DOI: 10.30598/barekengvol16iss4pp1313-1324

Abstract

One of the problems in modelling multivariate time series is stationary. Stationary test results do not always produce all stationary variables; mixed stationary and non-stationary variables are possible. When stationary problems are found in multivariate time series modelling, it is necessary to evaluate the model's performance in various stationary conditions to obtain the best forecasting model. This study aims to get a superior multivariate time series forecasting model based on the goodness of the model in various stationary conditions. In this study, the evaluation of the model's performance through simulation data modelling is then applied to the actual data with a stationary problem, namely Bogor City inflation data. The best model in simulation modelling is based on the stability of RMSE and MAD in 100 replications. The results are that the VAR model is the best in various stationary conditions. Meanwhile, the best model on actual data modelling is based on evaluation in 4 folds for model fitting power and model forecasting power. The Bogor City inflation data modelling with the mixed stationary problem resulted in the best model, namely the VAR(1) model. This means the VAR model is good enough to be used as a forecasting model in mixed stationary conditions. Thus, in this study, based on the goodness of the model in two modelling scenarios in various stationary conditions, overall, it was found that the VAR model was superior to the VARD and VECM models.
Co-Authors Embay Rohaeti I Made Sumertajaya Kusman Sadik