Haydier, E., Albarwari, N., Ali, T. (2023). The Comparison Between VAR and ARIMAX Time Series Models in Forecasting. Alustath, 20(2), 249-262. doi: 10.33899/iqjoss.2023.0181260
Esraa Awni Haydier; Nasradeen Haj Salih Albarwari; Taha Hussein Ali. "The Comparison Between VAR and ARIMAX Time Series Models in Forecasting". Alustath, 20, 2, 2023, 249-262. doi: 10.33899/iqjoss.2023.0181260
Haydier, E., Albarwari, N., Ali, T. (2023). 'The Comparison Between VAR and ARIMAX Time Series Models in Forecasting', Alustath, 20(2), pp. 249-262. doi: 10.33899/iqjoss.2023.0181260
Haydier, E., Albarwari, N., Ali, T. The Comparison Between VAR and ARIMAX Time Series Models in Forecasting. Alustath, 2023; 20(2): 249-262. doi: 10.33899/iqjoss.2023.0181260

The Comparison Between VAR and ARIMAX Time Series Models in Forecasting

IRAQI JOURNAL OF STATISTICAL SCIENCES
Volume 20, Issue 2, December 2023, Pages 249-262    PDF (1.57 M)
Document Type: Research Paper
DOI: 10.33899/iqjoss.2023.0181260
Authors
Esraa Awni Haydier* 1; Nasradeen Haj Salih Albarwari2; Taha Hussein Ali1
1Salahaddin University, College of Administration and Economics, Department of Statistics and Informatics. Erbil, Iraq
2Duhok University, College of Administration and Economics, Department of Statistics and Informatics, Duhok, Iraq
Abstract
The research presented a comparative study in time series analysis and forecasting using VAR models, which depend on the existence of a significant relationship between the studied variables, and ARIMAX models, which depend on the linear effect of the independent variables (model input) on the dependent variable (model output). The models were analyzed using time series data for the Iraqi general budget for the period (2004-2020), which represents foreign reserves and government spending. Time series of government expenditure was forecast for the years (2021-2024) and a comparison was made between the efficiency of the models estimated through the mean square error (MSE) criterion. The analysis was carried out using the MATLAB program, and the results of the analysis concluded that the VAR model was more efficient than the ARIMAX model for this data and the increase in foreign reserves and government spending for the Iraqi will continue during the coming period (2021-2024).

Highlights
  1. The VAR model is better than the ARIMAX model for this data depending on the MSE criterion.
  2. There is a simple linear correlation (and correlation) between foreign reserves and government spending to 91.4%, which is positive and significant.
  3. The forecast for the period (2021-2024) shows an increase in government spending.
  4. There is a general trend in the time series of foreign reserves and government spending indicating a significant increase in the sustainable deficit of the general budget in Iraq.
Keywords
Time Series; VAR; ARIMAX; Forecasting; Iraqi budget
References
References:

  1. ‏‏Al Wadi S., M. T. Ismail, and S. A. Abdulkarim, “Forecasting financial time ‎series database on wavelet transforms and ARIMA model” Regional ‎Conference on Applied and Engineering Mathematics, vol.4, pp.448-453, 2010.‎
  2. Ali, Taha Hussein & Awaz Shahab M. "Uses of Waveshrink in Detection and Treatment of Outlier Values in Linear Regression Analysis and Comparison with Some Robust Methods", Journal of Humanity Sciences 21.5 (2017): 38-61.
  3. Ali, Taha Hussein & Mardin Samir Ali. "Analysis of Some Linear Dynamic Systems with Bivariate Wavelets" Iraqi Journal of Statistical Sciences 16.3 (2019): 85-109.
  4. Ali, Taha Hussein & Qais Mustafa. "Reducing the orders of mixed model (ARMA) before and after the wavelet de-noising with application." Journal of Humanity Sciences 20.6 (2016): 433-442.
  5. Ali, Taha Hussein and Jwana Rostam Qadir. "Using Wavelet Shrinkage in the Cox Proportional Hazards Regression model (simulation study)", Iraqi Journal of Statistical Sciences, 19, 1, 2022, 17-29.
  6. Ali, Taha Hussein, "Estimation of Multiple Logistic Model by Using Empirical Bayes Weights and Comparing it with the Classical Metod with Application" Iraqi Journal of Statistical Sciences 20 (2011): 348-331.
  7. Ali, Taha Hussein, and Saleh, Dlshad Mahmood, "Proposed Hybrid Method for Wavelet Shrinkage with Robust Multiple Linear Regression Model: With Simulation Study" QALAAI ZANIST JOURNAL 7.1 (2022): 920-937.
  8. Ali, Taha Hussein, and Saleh, Dlshad Mahmood,"COMPARISON BETWEEN WAVELET BAYESIAN AND BAYESIAN ESTIMATORS TO REMEDY CONTAMINATION IN LINEAR REGRESSION MODEL" PalArch's Journal of Archaeology of Egypt/Egyptology 18.10 (2021): 3388-3409.
  9. Ali, Taha Hussein, Avan Al-Saffar, and Sarbast Saeed Ismael. "Using Bayes weights to estimate parameters of a Gamma Regression model." Iraqi Journal of Statistical Sciences 20.1 (2023): 43-54.
  10. Ali, Taha Hussein, Heyam Abd Al-Majeed Hayawi, and Delshad Shaker Ismael Botani. "Estimation of the bandwidth parameter in Nadaraya-Watson kernel non-parametric regression based on universal threshold level." Communications in Statistics-Simulation and Computation 52.4 (2023): 1476-1489.
  11. Ali, Taha Hussein, Nasradeen Haj Salih Albarwari, and Diyar Lazgeen Ramadhan. "Using the hybrid proposed method for Quantile Regression and Multivariate Wavelet in estimating the linear model parameters." Iraqi Journal of Statistical Sciences 20.1 (2023): 9-24.
  12. Ali, Taha Hussein, Saman Hussein Mahmood, and Awat Sirdar Wahdi. "Using Proposed Hybrid method for neural networks and wavelet to estimate time series model." Tikrit Journal of Administration and Economics Sciences 18.57 part 3 (2022).
  13. Ali, Taha Hussein. "Modification of the adaptive Nadaraya-Watson kernel method for nonparametric regression (simulation study)." Communications in Statistics-Simulation and Computation 51.2 (2022): 391-403.
  14. Ali, Taha Hussien, Nazeera Sedeek Kareem, and Awaz shahab mohammad, (2021), Data de-noise for Discriminant Analysis by using Multivariate Wavelets (Simulation with practical application), Journal of Arab Statisticians Union (JASU), 5.3: 78-87.
  15. Kareem, Nazeera Sedeek, Taha Hussein Ali, and Awaz shahab M, "De-noise data by using Multivariate Wavelets in the Path analysis with application", Kirkuk University Journal of Administrative and Economic Sciences, 10.1 (2020): 268-294.
  16. Kozłowski, B. (2005). “Time series denoising with wavelet transform”. Journal of Telecommunications and Information Technology, (3), 91-95.
  17. Mustafa, Qais, and Ali, Taha Hussein. "Comparing the Box Jenkins models before and after the wavelet filtering in terms of reducing the orders with application." Journal of Concrete and Applicable Mathematics 11 (2013): 190-198.
  18. Omar, Cheman, Taha Hussien Ali, and Kameran Hassn, Using Bayes weights to remedy the heterogeneity problem of random error variance in linear models, IRAQI JOURNAL OF STATISTICAL SCIENCES, 17, 2, 2020, 58-67.
  19. Percival, D. B., & Walden, A. T. (2000).” Wavelet methods for time series analysis and its statistical applications”. (pp. 1–613). doi.org/10.1017/cbo9780511841040.
  20. Raza, Mahdi Saber, Taha Hussein Ali, and Tara Ahmed Hassan. "Using Mixed Distribution for Gamma and Exponential to Estimate of Survival Function (Brain Stroke)." Polytechnic Journal 8.1 (2018).
  21. Shahla Hani Ali, Heyam A.A. Hayawi, Nazeera Sedeek K., and Taha Hussein Ali, (2023) "Predicting the Consumer price index and inflation average for the Kurdistan Region of Iraq using a dynamic model of neural networks with time series", The 7th International Conference of Union if Arab Statistician-Cairo, Egypt 8-9/3/2023:137-147.
  22. Zivot, E., Wang, J. (2003). Vector Autoregressive Models for Multivariate Time Series. In: Modeling Financial Time Series with S-Plus®. Springer, New York, NY.
  23. Ali, T. H., Sedeeq, B. S., Saleh, D. M., & Rahim, A. G. Robust multivariate quality control charts for enhanced variability monitoring. Quality and Reliability Engineering International (2023). https://doi.org/10.1002/qre.3472
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