Modeling and Analysis Data Production of Oil, and Oil and Gas in Indonesia by Using Threshold Vector Error Correction Model
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Keywords:
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Cointegration, Granger Causality, IRF, TVECM 2 Regime
Abstract
Data in the fields of finance, business, economics, agriculture, the environment and weather are commonly in the form of time series data. To analyze time series data that involves more than one variable (multivariate), vector autoregressive (VAR) models, vector autoregressive moving average (VARMA) models are generally used. If the variables discussed have cointegration, then the VAR model is modified into a vector error correction model (VECM). The relationship between short-term dynamics and deviation in the VECM model is assumed to be linear. If there is a nonlinear relationship between short-term dynamics and deviation, then a threshold vector error correction model (TVECM) can be used. The variables used in this research consist of oil production and Indonesian oil and gas production from January 2019 to March 2021. The research results show that the best model for data on oil production and oil and gas production is the TVECM 2 Regime model. Based on the TVECM 2 Regime model, further analysis, namely Granger causality and Impulse Response Function are discussed.
References
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Winarno, S., M. Usman, D. Kurniasari, and W. Widiarti (2021). Application of Vector Error Correction Model (VECM) and Impulse Response Function for Daily Stock Prices. In Journal of Physics: Conference Series, volume 1751. IOP Publishing, page 012016
Brockwell, P. J. and R. A. Davis (2002). Introduction to Time Series and Forecasting. Springer
Hamilton, J. (1994). Time Series Analysis, New Jersey, Princeton Uni. Princeton University Press
Hansen, B. E. and B. Seo (2002). Testing for Two-Regime Threshold Cointegration in Vector Error Correction Models. Journal of Econometrics, 110(2); 293–318
Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica: Journal of the Econometric Society, 59; 1551–1580
Johansen, S. and K. Juselius (1990). Maximum Likelihood Estimation and Inference on Cointegration-With Appucations to the Demand for Money. Oxford Bulletin of Economics and statistics, 52(2); 169–210
Kanjilal, K. and S. Ghosh (2017). Dynamics of Crude Oil and Gold Price Post 2008 Global Financial Crisis–New Evidence from Threshold Vector Error-Correction Model. Resources Policy, 52; 358-365
Kirchgässner, G., J. Wolters, and U. Hassler (2012). Introduction to Modern Time Series Analysis. Springer Science & Business Media
Loves, L., M. Usman, Warsono, Widiarti, and E. Russel (2021). Modeling Multivariate Time Series by Vector Error Correction Models (VECM) (Study: PT Kalbe Farma Tbk. and PT Kimia Farma (Persero) Tbk). In Journal of Physics: Conference Series, volume 1751. IOP Publishing, page 012013
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer Science & Business Media
Malik, K., H. Ajmal, and M. U. Zahid (2017). Oil Price Shock and Its Impact on the Macroeconomic Variables of Pakistan: A Structural Vector Autoregressive Approach. International Journal of Energy Economics and Policy, 7(5); 83–92
Peña, D., G. C. Tiao, and R. S. Tsay (2001). A Course in Time Series Analysis, volume 409. Wiley Online Library
Rachev, S. T., S. Mittnik, F. J. Fabozzi, S. M. Focardi, and T. J. Ić (2007). Financial Econometrics: From Basics to Advanced Modeling Techniques. John Wiley & Sons
Reinsel, G. (1993). Multivariate Time Series Analysis. John Wiley & Sons New York, NY
Sharma, A., S. Giri, H. Vardhan, S. Surange, R. Shetty, and V. Shetty (2018). Relationship between Crude Oil Prices and Stock Market: Evidence from India. International Journal of Energy Economics and Policy, 8(4); 331
Siggiridou, E. and D. Kugiumtzis (2015). Granger Causality in Multivariate Time Series Using a Time-Ordered Restricted Vector Autoregressive Model. IEEE Transactions on Signal Processing, 64(7); 1759–1773
Sohibien, G. P. D. (2017). Application of Threshold Vector Error Correction Model (TVECM) in Describing Adjustment of Interest Rate of Working Capital Credit to BI Rate Movement. Journal of Science and Science Education, 1(2); 31–43
Tsay, R. S. (2005). Analysis of Financial Time Series. John Wiley & Sons
Tsay, R. S. (2013). Multivariate Time Series Analysis: With R and Financial Applications. John Wiley & Sons
Usman, M., D. F. Fatin, M. Y. S. Barusman, and F. A. Elfaki (2017). Application of Vector Error Correction Model (VECM) and Impulse Response Function for Analysis Data Index of Farmers’ Terms of Trade. Indian Journal of Science and Technology, 10(19); 1–14
Usman, M., L. Loves, E. Russel, M. Ansori, W. Warsono, W. Widiarti, and W. Wamiliana (2022). Analysis of Some Energy and Economics Variables by Using VECMX Model in Indonesia. International Journal of Energy Economics and Policy, 12(2); 91–102
Warsono, W., E. Russel, A. Putri, W. Wamiliana, W. Widiarti, and M. Usman (2020). Dynamic Modeling Using Vector Error-correction Model Studying the Relationship Among Data Share Price of Energy PGAS Malaysia, AKRA, Indonesia, and PTT PCL-Thailand. International Journal of Energy Economics and Policy, 10(2); 360–373
Warsono, W., E. Russel, W. Wamiliana, W. Widiarti, and M. Usman (2019). 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
Wei, W. W. (2006). Univariate and Multivariate Methods. Addison-Wesley Publishing Company
Wei, W. W. (2018). Multivariate Time Series Analysis and Applications. John Wiley & Sons
Winarno, S., M. Usman, D. Kurniasari, and W. Widiarti (2021). Application of Vector Error Correction Model (VECM) and Impulse Response Function for Daily Stock Prices. In Journal of Physics: Conference Series, volume 1751. IOP Publishing, page 012016
Authors
Widiarti, Usman, M., Putri, A. R., & Russel, E. (2024). Modeling and Analysis Data Production of Oil, and Oil and Gas in Indonesia by Using Threshold Vector Error Correction Model. Science and Technology Indonesia, 9(1), 189–197. https://doi.org/10.26554/sti.2024.9.1.189-197
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