Econometric Analysis and Forecasting of Madagascar’s Economy: An ARIMAX Approach

ANDRIANADY, Josué R. and Randriamifidy, Fitiavana M. and Ranaivoson, Michel H. P. and Steffanie, Thierry Miora (2023): Econometric Analysis and Forecasting of Madagascar’s Economy: An ARIMAX Approach.

Abstract

This research conducts an in-depth econometric analysis of key economic indicators in Madagascar, with a specific focus on Gross Domestic Product (GDP) and the USD exchange rate (USD/MGA). Employing the rigorous Box-Jenkins methodology with ARIMAX modeling, we meticulously examine historical trends, model time series data, and provide forecasts for the year 2023. Our analysis notably highlights a projected decline in Madagascar’s GDP for the year 2023, shedding light on the potential repercussions of various factors such as impending presidential elections and ongoing challenges like electricity shortages. These factors have the potential to exert a significant influence on the trajectory of the country’s economy. While ARIMAX models constitute invaluable tools for forecasting, we underscore the necessity of incorporating a more expansive array of econometric methodologies to bolster economic resilience and inform policymaking. This study underscores the critical importance of combining data-driven modeling with a profound understanding of the contextual intricacies that characterize Madagascar’s intricate economic landscape. Moreover, it extends its relevance to other emerging economies facing similar complexities and challenges.

• Item Type: MPRA Paper
• Language: English
• Keywords: Econometrics, ARIMAX modeling, ARIMA modeling, Madagascar, Gross Domestic Product, USD exchange rate, Forecasting, Economic Analysis, Box-Jenkins methodology, Time Series Data.
• Subjects: A - General Economics and Teaching > A1 - General Economics; A - General Economics and Teaching > A1 - General Economics > A10 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling

References

1] ANDRIANADY R. Josué. (2023). “Crunching the Numbers: A Comparison of Econometric Models for GDP Forecasting in Madagascar”.

[2] Pierre, A. A., Akim, S. A., Semenyo, A. K., & Babiga B. (2023). Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches. Energies, 16(12). 4739–4739.

[3] Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control.

[4] Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting. Springer. [5] Chopra, S., & Meindl, P. (2007). Supply Chain Management: Strategy, Planning, and Operation. Pearson Prentice Hall. 9

[6] Nanthiya, D., Gopal, S. B., Balakumar, S., Harisankar, M., & Midhun, S. P. (2023, May). “Gold Price Prediction using ARIMA model”. In 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN). 1–6.

[7] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice,(2nd ed.). OTexts.

[8] Makridakis, S. & Hibon, M. (1997). “ARMA models and the Box–Jenkins methodology”, Journal of Forecasting, 16(3), 147–163.

[9] Navaneetha K. M., Ranjith, R. ,& Lavanya, B. (2022). “Climate Change Prediction Using ARIMA Model”. International Journal For Science Technology And Engineering, 10(6). 621–625.

[10] Office malgache des Hydrocarbures, http://www.omh.mg/index.php?idm=2&CL= consoannuel

[11] Alotaibi R. (2022). “ARIMA Model for Stock Market Prediction”. ICCTA ’22: Proceedings of the 2022 8th International Conference on Computer Technology Applications. 1–4.

[12] Tsay, R. S. (2010). Analysis of Financial Time Series,( 3rd ed.). Wiley.

[13] Wilks, D. S. (2011). Statistical Methods in the Atmospheric Sciences,(3rd ed.). Academic Press.

[14] Williams, B. M. (2001). “Multivariate vehicular traffic flow prediction: evaluation of ARIMAXmodeling”. Transportation Research Record, 1776(1). 194–200.

[15] Xie, J. (2023, Mar). “Identifying Optimal Indicators and Lag Terms for Nowcasting Models”. IMF Working Papers, 2023(045). 38.

[16] Yule, G. U. (1927). “On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer’s Sunspot Numbers”. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 226. 267–298.