How to cite this paper
Sohibien, G., Setiawa, S & Prastyo, D. (2024). Data forecasting performance evaluation of threshold spatial vector autoregressive with exogenous variables.International Journal of Data and Network Science, 8(1), 523-536.
Refrences
Andreas, C., Faricha, A., Ulyah, S. M., Susanti, R., Mardhiana, H., Nanda, M. A., & Firman Adi, R. (2022). Comparison study using ARIMAX and VARX in cash flow forecasting. AIP Conference Proceedings, 2641(December). https://doi.org/10.1063/5.0118519
Andrés-Rosales, R., Quintana-Romero, L., de Jesús-Almonte, L., & del Río-Rama, M. de la C. (2021). Spatial spillovers of economic growth and public spending in Mexico: Evidence from a SpVAR model, 1999–2019. Economic Analysis and Policy, 71, 660–673. https://doi.org/10.1016/j.eap.2021.07.004.
Balke, N. S., & Fomby, T. (1997). Threshold Cointegration. International Economic Review, 38, 627–645. http://www.jstor.org.libezp.utar.edu.my/stable/pdfplus/2527201.pdf.
Bickel, P. J., & Doksum, K. A. (2013). Mathematical statistics, Vol. 1, Second Edition, CRC Press, New York.
Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric Statistical Inference. CRC Press, New York.
Illowsky, B., College, D. A., & Dean, S. (2013). Introductory Statistics. In Journal of Chemical Information and Modeling, Vol. 53, Issue 9, OpenStax.
Jiang, Y., Wang, G. J., Ma, C., & Yang, X. (2021). Do credit conditions matter for the impact of oil price shocks on stock returns? Evidence from a structural threshold VAR model. International Review of Economics and Finance, 72(November 2018), 1–15. https://doi.org/10.1016/j.iref.2020.10.019
Kong, L., Li, G., Rafique, W., Shen, S., He, Q., Khosravi, M. R., Wang, R., & Qi, L. (2022). Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(8), 1–10. https://doi.org/10.1109/TCBB.2022.3205064
Rajab, K., Kamalov, F., & Cherukuri, A. K. (2022). Forecasting COVID-19: Vector Autoregression-Based Model. Arabian Journal for Science and Engineering, 47(6), 6851–6860. https://doi.org/10.1007/s13369-021-06526-2
Ramajo, J., Márquez, M. A., & Hewings, G. J. D. (2017). Spatiotemporal Analysis of Regional Systems: A Multiregional Spatial Vector Autoregressive Model for Spain. International Regional Science Review, 40(1), 75–96. https://doi.org/10.1177/0160017615571586
Sohibien, G. P. D. (2018). Analysis of the effect of fuel price policy on Jakarta inflation by using multi-input intervention model. AIP Conference Proceedings, 2014(September). https://doi.org/10.1063/1.5054529
Sohibien, G. P. D., Laome, L., Choiruddin, A., & Kuswanto, H. (2022). COVID-19 Pandemic’s Impact on Return on Asset and Financing of Islamic Commercial Banks: Evidence from Indonesia. Sustainability, 14(3), 1–13. https://doi.org/10.3390/su14031128
Stigler, M. (2010). Threshold cointegration: overview and implementation in R. http://r.meteo.uni.wroc.pl/web/packages/tsDyn/vignettes/ThCointOverview.pdf
Tsagkanos, A., Evgenidis, A., & Vartholomatou, K. (2018). Financial and monetary stability across Euro-zone and BRICS: An exogenous threshold VAR approach. Research in International Business and Finance, 44, 386–393. https://doi.org/10.1016/j.ribaf.2017.07.108
Tsayو R. S (2014). Multivariate Time Series Analysis with R and Financial Applications, Wiley, New Jersey.
Tsay, R. S. (2010). Analysis of financial time series, Third Edition, Wiley, New Jersey.
Wang, D., Zheng, Y., Lian, H., & Li, G. (2021). High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition. Journal of the American Statistical Association, Vol. 117, No. 539, 1338–1356. https://doi.org/10.1080/01621459.2020.1855183
Wei, W. W. S. (2006). Time Series Analysis Univariate and Multivariate Methods, Second Edition, Pearson, New York.
Yuhan, R. J., & Sohibien, G. P. D. (2018). Relationship between inflation, exchange rate, and money supply in Indonesia using threshold vector autoregressive (TVAR). AIP Conference Proceedings, 2014. https://doi.org/10.1063/1.5054532.
Zhu, H. (2021). Real-time prognostics of engineered systems under time-varying external conditions based on the COX PHM and VARX hybrid approach. Sensors, 21(5), 1–23. https://doi.org/10.3390/s21051712.
Andrés-Rosales, R., Quintana-Romero, L., de Jesús-Almonte, L., & del Río-Rama, M. de la C. (2021). Spatial spillovers of economic growth and public spending in Mexico: Evidence from a SpVAR model, 1999–2019. Economic Analysis and Policy, 71, 660–673. https://doi.org/10.1016/j.eap.2021.07.004.
Balke, N. S., & Fomby, T. (1997). Threshold Cointegration. International Economic Review, 38, 627–645. http://www.jstor.org.libezp.utar.edu.my/stable/pdfplus/2527201.pdf.
Bickel, P. J., & Doksum, K. A. (2013). Mathematical statistics, Vol. 1, Second Edition, CRC Press, New York.
Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric Statistical Inference. CRC Press, New York.
Illowsky, B., College, D. A., & Dean, S. (2013). Introductory Statistics. In Journal of Chemical Information and Modeling, Vol. 53, Issue 9, OpenStax.
Jiang, Y., Wang, G. J., Ma, C., & Yang, X. (2021). Do credit conditions matter for the impact of oil price shocks on stock returns? Evidence from a structural threshold VAR model. International Review of Economics and Finance, 72(November 2018), 1–15. https://doi.org/10.1016/j.iref.2020.10.019
Kong, L., Li, G., Rafique, W., Shen, S., He, Q., Khosravi, M. R., Wang, R., & Qi, L. (2022). Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(8), 1–10. https://doi.org/10.1109/TCBB.2022.3205064
Rajab, K., Kamalov, F., & Cherukuri, A. K. (2022). Forecasting COVID-19: Vector Autoregression-Based Model. Arabian Journal for Science and Engineering, 47(6), 6851–6860. https://doi.org/10.1007/s13369-021-06526-2
Ramajo, J., Márquez, M. A., & Hewings, G. J. D. (2017). Spatiotemporal Analysis of Regional Systems: A Multiregional Spatial Vector Autoregressive Model for Spain. International Regional Science Review, 40(1), 75–96. https://doi.org/10.1177/0160017615571586
Sohibien, G. P. D. (2018). Analysis of the effect of fuel price policy on Jakarta inflation by using multi-input intervention model. AIP Conference Proceedings, 2014(September). https://doi.org/10.1063/1.5054529
Sohibien, G. P. D., Laome, L., Choiruddin, A., & Kuswanto, H. (2022). COVID-19 Pandemic’s Impact on Return on Asset and Financing of Islamic Commercial Banks: Evidence from Indonesia. Sustainability, 14(3), 1–13. https://doi.org/10.3390/su14031128
Stigler, M. (2010). Threshold cointegration: overview and implementation in R. http://r.meteo.uni.wroc.pl/web/packages/tsDyn/vignettes/ThCointOverview.pdf
Tsagkanos, A., Evgenidis, A., & Vartholomatou, K. (2018). Financial and monetary stability across Euro-zone and BRICS: An exogenous threshold VAR approach. Research in International Business and Finance, 44, 386–393. https://doi.org/10.1016/j.ribaf.2017.07.108
Tsayو R. S (2014). Multivariate Time Series Analysis with R and Financial Applications, Wiley, New Jersey.
Tsay, R. S. (2010). Analysis of financial time series, Third Edition, Wiley, New Jersey.
Wang, D., Zheng, Y., Lian, H., & Li, G. (2021). High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition. Journal of the American Statistical Association, Vol. 117, No. 539, 1338–1356. https://doi.org/10.1080/01621459.2020.1855183
Wei, W. W. S. (2006). Time Series Analysis Univariate and Multivariate Methods, Second Edition, Pearson, New York.
Yuhan, R. J., & Sohibien, G. P. D. (2018). Relationship between inflation, exchange rate, and money supply in Indonesia using threshold vector autoregressive (TVAR). AIP Conference Proceedings, 2014. https://doi.org/10.1063/1.5054532.
Zhu, H. (2021). Real-time prognostics of engineered systems under time-varying external conditions based on the COX PHM and VARX hybrid approach. Sensors, 21(5), 1–23. https://doi.org/10.3390/s21051712.