Analysis Multivariate Time Series Using State Space Model for Forecasting Inflation in Some Sectors of Economy in Indonesia
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Keywords:
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Inflation, State Space, Vector Autoregressive, State Vector, Granger Causality, Forecasting
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.
References
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Aoki, M. and A. Havenner (1991) State space modelling of multiple time series, Econometric Reviews, 10, 1–59.
Durbin, J. and S. J. Koopman (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press.
Durbin, J., (2004), Introduction to state space time series analysis , in Andrew Harvey, Koopman ,S.J., and Neil Shephard,(Editors) (2004), State Space And Unobserved Component Models Theory and Applications, London, Cambridge University Press.
Durbin, J., and Koopman, S. J. (2012). Time Series Analysis by State Space Methods. (2nd ed.), Oxford University Press, Oxford.
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Harvey, A.C. (1993). Time Series Models (2 edn). Hemel Hempstead: Harvester Wheatsheaf.
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Kementerian Perdagangan. (2022). https://satudata.kemendag.go.id/data-informasi/perdagangan-dalam-negeri/inflasi-2020.
Kim, C.-J. and C. R. Nelson (1999). State-Space Models with Regime Switching. Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA: MIT.
Lai, T. L., & Bukkapatanam, V. (2013). Adaptive filtering, nonlinear state-space models, and applications in finance and econometrics. In State-Space Models (pp. 3-22). Springer, New York, NY.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer Verlaag, Berlin.
Russel, E., Kesumah, F. S. D., Rialdi, A., Nairobi, & Usman, M. (2020). Dynamic modeling and forecasting stock price data by applying AR-GARCH model. TEST Engineering and Management, 82, 6829-6842.
Russel, E., Wamiliana, Nairobi, Warsono, Usman, M., & Daoud, J. I. (2022). Dynamic Modeling and Forecasting Data Energy Used and Carbon Dioxide (CO2). Science and Technology Indonesia, 7(2), 228-237.
Shumway, R.H. and D.S. Stoffer (2000). Time Series Analysis and Its Applications. New York, NY: Springer-Verlag.
Tsay, R. S. (2005). Analysis of Financial Time Series. John Wiley & Sons
Tsay, R.S. (2014). Multivariate Time Series Analysis. A John Wiley & Sonc, Inc Publication, New York.
Warsono, Russel, E., Wamiliana, Widiarti, and Usman, M. (2019a). Modeling and Forecasting by The Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia Over The Years 2002-2017). International Journal of Energy Economics and Policy, 9(4); 240–247.
Warsono, Russel, E., W. Wamiliana, Widiarti, and Usman, M. (2019b). Vector Autoregressive with Exogenous Variable Model and its Application in Modeling and Forecasting Energy Data: Case Study of PTBA and HRUM Energy. International Journal of Energy Economics and Policy, 9(2); 390–398
Warsono, Russel, E., Putri, A.R., Wamiliana, Widiarti, Usman, M. (2020). Dynamic modeling using vector error-correction model: Studying the relationship among data share price of energy PGAS Malaysia, AKRA, Indonesia, and PPTT-PCL-Thailand. International Journal of Energy Economics and Policy, 10(2), 360-373.
Wei, William. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods. (2nd ed.) New York: Pearson Education.
Welch, G. dan Bishop, G. (2001). An Introduction to the Kalman Filter. University of North Carolina, Chapel Hill.
Akaike, H, (1976), Canonical Correlation Analysis of Time Series and the Use of an Information Criterion, in R. Mehra and K. Lainiotis, eds., System Identification: Advances and Case Studies, Academic Press, Inc., New York.
Aoki, M. and A. Havenner (1991) State space modelling of multiple time series, Econometric Reviews, 10, 1–59.
Durbin, J. and S. J. Koopman (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press.
Durbin, J., (2004), Introduction to state space time series analysis , in Andrew Harvey, Koopman ,S.J., and Neil Shephard,(Editors) (2004), State Space And Unobserved Component Models Theory and Applications, London, Cambridge University Press.
Durbin, J., and Koopman, S. J. (2012). Time Series Analysis by State Space Methods. (2nd ed.), Oxford University Press, Oxford.
Gomez, V.,(2016). Multivariate Time Series With Linear State Space Structure. Switzerland: Springer Verlaq.
Hamilton, J. (1994). Time Series Analysis. Princeton, NJ: Princeton University Press.
Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
Harvey, A.C. (1993). Time Series Models (2 edn). Hemel Hempstead: Harvester Wheatsheaf.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems [J]. Journal of basic Engineering, 82(1), 35-45.
Kementerian Perdagangan. (2022). https://satudata.kemendag.go.id/data-informasi/perdagangan-dalam-negeri/inflasi-2020.
Kim, C.-J. and C. R. Nelson (1999). State-Space Models with Regime Switching. Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA: MIT.
Lai, T. L., & Bukkapatanam, V. (2013). Adaptive filtering, nonlinear state-space models, and applications in finance and econometrics. In State-Space Models (pp. 3-22). Springer, New York, NY.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer Verlaag, Berlin.
Russel, E., Kesumah, F. S. D., Rialdi, A., Nairobi, & Usman, M. (2020). Dynamic modeling and forecasting stock price data by applying AR-GARCH model. TEST Engineering and Management, 82, 6829-6842.
Russel, E., Wamiliana, Nairobi, Warsono, Usman, M., & Daoud, J. I. (2022). Dynamic Modeling and Forecasting Data Energy Used and Carbon Dioxide (CO2). Science and Technology Indonesia, 7(2), 228-237.
Shumway, R.H. and D.S. Stoffer (2000). Time Series Analysis and Its Applications. New York, NY: Springer-Verlag.
Tsay, R. S. (2005). Analysis of Financial Time Series. John Wiley & Sons
Tsay, R.S. (2014). Multivariate Time Series Analysis. A John Wiley & Sonc, Inc Publication, New York.
Warsono, Russel, E., Wamiliana, Widiarti, and Usman, M. (2019a). Modeling and Forecasting by The Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia Over The Years 2002-2017). International Journal of Energy Economics and Policy, 9(4); 240–247.
Warsono, Russel, E., W. Wamiliana, Widiarti, and Usman, M. (2019b). Vector Autoregressive with Exogenous Variable Model and its Application in Modeling and Forecasting Energy Data: Case Study of PTBA and HRUM Energy. International Journal of Energy Economics and Policy, 9(2); 390–398
Warsono, Russel, E., Putri, A.R., Wamiliana, Widiarti, Usman, M. (2020). Dynamic modeling using vector error-correction model: Studying the relationship among data share price of energy PGAS Malaysia, AKRA, Indonesia, and PPTT-PCL-Thailand. International Journal of Energy Economics and Policy, 10(2), 360-373.
Wei, William. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods. (2nd ed.) New York: Pearson Education.
Welch, G. dan Bishop, G. (2001). An Introduction to the Kalman Filter. University of North Carolina, Chapel Hill.
Authors
Russel, E., Wamiliana, W., Warsono, Nairobi, Usman, M., & Elfaki, F. A. (2023). Analysis Multivariate Time Series Using State Space Model for Forecasting Inflation in Some Sectors of Economy in Indonesia. Science and Technology Indonesia, 8(1), 144–150. https://doi.org/10.26554/sti.2023.8.1.144-150
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