Comparative Analysis of ARIMA, VAR, and Linear Regression Models for UAE GDP Forecasting

McCloskey, PJ and Malheiros Remor, Rodrigo (2024): Comparative Analysis of ARIMA, VAR, and Linear Regression Models for UAE GDP Forecasting.

Abstract

Forecasting GDP is crucial for economic planning and policymaking. This study compares the performance of three widely-used econometric models—ARIMA, VAR, and Linear Regression—using GDP data from the UAE. Employing a rolling forecast approach, we analyze the models’ accuracy over different time horizons. Results indicate ARIMA’s robust long-term forecasting capability, LR models perform better with short-term predictions, particularly when exogenous variable forecasts are accurate. These insights provide a valuable foundation for selecting forecasting models in the UAE’s evolving economy, suggesting ARIMA’s suitability for long-term outlooks and LR for short-term, scenario-based forecasts.

• Item Type: MPRA Paper
• Language: English
• Keywords: GDP forecasting, ARIMA, VAR, Linear Regression, UAE economy
• Subjects: O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O10 - General; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General

References

Andrianady, J. R. (2023). Crunching the Numbers: A Comparison of Econometric Models for GDP Forecasting in Madagascar. Munich Personal RePEc Archive Paper No. 120698.

Bäurle, G., Steiner, E., Züllig, G. (2020). Forecasting the production side of GDP. Wiley, 10.1002/for.2725.

Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control (Revised Edition). Holden Day: San Francisco.

Ghazo, A. (2021). Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy. International Journal of Financial Research, Vol. 12, No. 3, Special Issue.

IRENA (2024). Energy Profile United Arab Emirates. IRENA: Abu Dhabi.

Ma, Y. (2024). Analysis and Forecasting of GDP Using the ARIMA Model. Clausius Scientific Press, Vol. 5, No. 1.

Maccarrone, G., Morelli, G., Spadaccini, S., (2021). GDP Forecasting: Machine Learning, Linear or Autoregression?. Frontier Artificial Intelligence. 4:757864.

Mankiw N.G. (2023) Macroeconomics (11th ed.). Macmillan Learning: Gordonsville.

Muma, B. and Karoki, A. (2022) Modeling GDP Using Autoregressive Integrated Moving Average (ARIMA) Model: A Systematic Review. Open Access Library Journal, 9: e8355.

Quaadi I, Ibourk A. (2022). The Contribution of Deep Learning Models: Application of LSTM to Predict the Moroccan GDP Growth Using Drought Indexes. International Conference on Advanced Intelligent Systems for Sustainable Development, Springer, pp. 284-294.

Robertson, J. C., Tallman, E. W. (1999). Vector Autoregressions: Forecasting and Reality. Federal Reserve Bank of Atlanta, Economic Review, First Quarter 1999.

Roush, J., Siopes, K., Hu, G. (2017). Predicting Gross Domestic Product Using Autoregressive Models. Central Michigan University, USA.

Ryadh M. Alkhareif, R.M. (2018). Nowcasting Real GDP for Saudi Arabia. Saudi Arabian Monetary Authority, WP/18/2.

Shahini, L., Haderi, S. (2013). Short Term Albanian GDP Forecast: “One Quarter to One Year Ahead”. European Scientific Journal, December 2013 edition, Vol.9, No.34.

Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.

Stock, J.H., Watson, M.W. (2020). Introduction to Econometrics (4th ed.). Pearson: London

World Bank (2020). Forecasting Moldovan GDP With VAR Models. World Bank, Washington, USA.

Zhang Y, Shang W, Zhang N, et al (2023). Quarterly GDP Forecast Based on Coupled Economic and Energy Feature WA-LSTM Model. Frontiers in Energy Research, 11: 1329376.

Ziyuan, C. (2023). Analysis of economic growth forecast based on regression model. MMACS, Highlights in Science, Engineering and Technology, Vol. 42.