Forewarned is forearmed: Forecasting expansions and contractions of the Saudi Economy

Year 2023, Volume: 8 Issue: 1, 178 - 190, 30.06.2023

Pierre Rostan , Alexandra Rostan , Mohammad Nurunnabi

Abstract

In this study, it is tried to determine the expansions and contractions of the Saudi economy between 2021 and 2060. The Saudi economy being oil-driven, the special relationship between oil price and Saudi Real GDP is unfolded with Multiscale Principal Component Analysis (MPCA) combined with wavelet analysis. Assuming two scenarios with and without MPCA, the more likely midpoint scenario returns a 2021-2060 average of the Real GDP annual growth rate of - 0.37%. Saudi Arabia is benchmarked to Iran. 2021-2060 estimates of oil price forecast a rebound after 2020 that will pull up Saudi Arabia’s GDP. However, in 2031, Saudi Arabia’s and Iran’s GDP growth rates will diverge, Iran’s growth rate remaining in positive territory until 2044, whereas Saudi economy enduring a lengthy recession until 2048. After 2048, the two economies will emerge from recession but will eventually return to it before 2060.

Keywords

Wavelet Analysis, Multiscale Principal Component Analysis, Saudi Arabia

Supporting Institution

N/A

Tedbiri Elden Bırakma: Suudi Ekonomisine İlişkin Genişleme Ve Daralma Tahminleri

Abstract

Bu çalışmada, Suudi ekonomisinin 2021 ile 2060 yılları arasındaki genişlemeleri ve daralmaları belirlenmeye çalışılmaktadır. Suudi ekonomisi petrole dayalı olduğundan, petrol fiyatı ile Suudi Reel GSYH'sı arasındaki özel ilişki, dalgacık analiziyle birleştirilmiş Çok Ölçekli Temel Bileşen Analizi (MPCA) ile ortaya konulmaya çalışılmıştır. MPCA analizini içeren ve içermeyen iki senaryo varsayıldığında, daha olası olan orta nokta senaryosu, 2021-2060 ortalama Reel GSYH yıllık büyüme oranını - %0,37 olarak göstermektedir. Suudi Arabistan, İran ile kıyaslandığında 2021-2060 petrol fiyatı tahminleri, 2020'den sonra Suudi Arabistan'ın GSYH'sını yukarı çekecek bir toparlanma öngörülmektedir. Ancak 2031'de Suudi Arabistan ve İran'ın GSYH büyüme oranları farklılaşmaktadır. İran'ın büyüme oranı 2044'e kadar pozitif bölgede kalırken, Suudi ekonomisinin 2048'e kadar uzun bir durgunluk yaşayacağı görülmektedir. 2048'den sonra iki ekonomi durgunluktan çıkacak ama 2060 öncesinde geri döneceklerdir.

Keywords

Dalgacık Analizi, Çok Ölçekli Temel Bileşen Analizi, Suudi Arabistan

References

• Abdel-Latif, H., R.H. Osman, & Ahmed, H. (2018). Asymmetric impacts of oil price shocks on government expenditures: Evidence from Saudi Arabia. Cogent Economics and Finance, 6(1), Retrieved from: https://doi.org/10.1080/23322039.2018.1512835 .
• Al-Nakib, O. (2015). Saudi Arabia: Economy resilient but growth slowing amid the oil price slump. Macroeconomic Outlook, Economic Update series, NKB Saudi Arabia.
• Al Rasasi, M., Qualls, J.H., & Almutairi, S. (2019). Testing for Causality between Oil Prices and Money Supply in Saudi Arabia. Saudi Arabian Monetary Authority working paper,
• Retrieved from: http://www.sama.gov.sa/en-US/EcomicResearch/WorkingPapers/Testing%20for%20Causality%20Between%20Oil%20Prices%20and%20Money%20Supply%20in%20Saudi%20Arabia.pdf .
• Arouri, M.E.H., & Nguyen. D.K. (2010). Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade. Energy Policy, 38 (8), 4528-4539.
• Baillie, R., & Bollerslev, T. (1992). Prediction in Dynamic Models with Time-Dependent Conditional Variances. Journal of Econometrics, 52(9), pp. 1-113.
• Berger, T. (2016). A wavelet analysis: Forecasting based on decomposed financial return series. Journal of Forecasting, 35(5), 419-433, doi:10.1002/for.2384
• Boyer, M.M., & Filion, D. (2009). Common and fundamental factors in stock returns of Canadian oil and gas companies. Energy Economics, 29, 428-453.
• Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Revised Edition. Holden Day, San Francisco, CA.
• Box, G.E.P., Jenkins, G.M., & Reinsel, G.C. (1994). Time Series Analysis: Forecasting and Control, 3rd ed. Prentice Hall, Englewood Cliffs.
• Broadstock, D.C., Cao, H., & Zhang, D. (2012). Oil shocks and their impact on energy related stocks in China. Energy Economics, 34(6), 1888-1895.
• Burg, J.P. (1975). Maximum Entropy Spectral Analysis, Retrieved from: http://sepwww.stanford.edu/theses/sep06/.
• Castells, F., Laguna, P., Sörnmo, L., Bollmann, A. & Roig, J.M. (2007). Principal Component Analysis in ECG Signal Processing. EURASIP Journal on Advances in Signal Processing, doi: 10.1155/2007/74580.
• Ciner, C. (2001). On the long run relationship between gold and silver prices: A note. Global Finance Journal, 12(2), 299-303.
• Conejo, A.J., Plazas, M.A., Espila, R., & Molina, A.B. (2005). Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power Systems, 20(2), 1035-1042, doi:10.1109/TPWRS.2005.846054
• Corinthios, M. (2009). Signals, Systems, Transforms, and Digital Signal Processing with MATLAB, Taylor and Francis Group, LLC CRC Press, Boca Raton, FL.
• Daubechies, I. (1994). Ten lectures on wavelets, CBMS, SIAM, 61, 198-202 and 254-256.
• Diebold, F., & Li, C. (2006). Forecasting the term structure of government bond yields, Journal of Econometrics, 130, 337–364.
• Durbin, J. (1960). The fitting of time series models. Revue de l'Institut International de Statistique, 28, 233-44.
• ElKholy, L. (2017). What we kw about the place in Egypt where Prophet Yusuf lived, Alarabiya News. Retrieved from: https://english.alarabiya.net/features/2017/07/14/Kw-the-place-where-Prophet-Joseph-lived-in-Egypt .
• El-Sharif, I., Brown, D., Burton, B., Nixon, B., & Russell, A. (2005). Evidence on the nature and extent of the relationship between oil prices and equity values in the UK. Energy Economics, 27(6), 819-830.
• Elyasiani, E., Mansur, I., & Odusami, B. (2011). Oil price shocks and industry stock returns. Energy Economics, 33(5), 966-974.
• Faff, R.W., & Brailsford, T.J. (1999). Oil price risk and the Australian stock market. Journal of Energy Finance and Development, 4(1), 69-78.
• Fang, C-R, & You, S-Y (2014). The impact of oil price shocks on the large emerging countries' stock prices: Evidence from China, India and Russia. International Review of Economics and Finance, 29, 330-338.
• FHI. (2021). Overview of Economic Forecasting Methods, Retrieved from: http://www.fhi.sk/files/katedry/kove/predmety/Progsticke_modely/Methods_basics.pdf .
• Filis, G. (2010). Macro economy, stock market, and oil prices: Do meaningful relationships exist among their cyclical fluctuations? Energy Economics, 32(4), 877-886.
• Forbes. (2018). Best Countries for Business: Saudi Arabia, Retrieved from: https://www.forbes.com/places/saudi-arabia/
• Fred (Federal Reserve Bank of St. Louis). (2023). Federal Reserve Economic Data, Retrieved from: https://fred.stlouisfed.org .
• Gupta, K. (2016). Oil price shocks, competition, and oil and gas stock returns — Global evidence. Energy Economics, 57, 140-153. ISSN 0140-9883, Retrieved from: https://doi.org/10.1016/j.eneco.2016.04.019 .
• He, K., Wang, L., Zou, Y., & Lai, K. (2014). Exchange rate forecasting using entropy optimized multivariate wavelet denoising model. Mathematical Problems in Engineering, 2014, 1-9, doi:10.1155/2014/389598
• Hemrit, W., & Benlagha, N. (2018). The impact of government spending on ilgdp in Saudi Arabia (multiplier analysis). International Journal of Economics and Business Research, 15(3), 350–372, doi:10.1504/IJEBR.2018.091050
• Huang, R.D., Masulis, R.W., & Stoll, H.R. (1996). Energy shocks and financial markets. Journal of Futures Markets, 16(1), 1-38.
• Holy Quran. (2023). Surah Yusuf. Retrieved from: https://www.clearquran.com/012.html
• IEEE. (2019), IEEE Transactions on Signal Processing, Retrieved from: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78
• Kang, W., Ratti, R.A. & Yoon, K.H. (2015). Time-varying effect of oil market shocks on the stock market. Journal of Banking and Finance, 61(2), 150-S163, ISSN 0378-4266, Retrieved from: https://doi.org/10.1016/j.jbankfin.2015.08.027. .
• Kao, L., Chiu, C., Lu, C., & Chang, C. (2013). A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Decision Support Systems, 54(3), 1228-1244, doi:10.1016/j.dss.2012.11.012
• Kriechbaumer, T., Angus, A., Parsons, D., & Casado, M. (2014). An improved wavelet-ARIMA approach for forecasting metal prices. Resources Policy, 39, 32-41, doi:10.1016/j.resourpol.2013.10.005
• Lee, D.T.L., & Yamamoto, A. (1994). Wavelet Analysis, theory and applications. Hewlett-Packard Journal, 44-52.
• Levinson, N. (1946). The Wiener RMS (root mean square) error criterion in filter design and prediction. Journal of Mathematical Physics, 25, 261-78.
• Misiti, M., Misiti, Y., Oppenheim, G. & Poggi, J.M. (2015). Wavelet Toolbox For Use with MATLAB, User's guide. The MathWorks, Natick, MA.
• Moshashai, D., Leber, A.M., & Savage, J.D. (2018). Saudi Arabia plans for its economic future: Vision 2030, the National Transformation Plan and Saudi fiscal reform. British Journal of Middle Eastern Studies, doi: 10.1080/13530194.2018.1500269
• Mseddi, S., & Benlagha, N. (2017). Linkage between energy consumption and economic growth: Evidence from Saudi Arabia. The Empirical Economics Letters, 16(10).
• OECD. (2021). Real GDP long-term forecast, Million US dollars, 2020 – 2060. OECD Economic Outlook: Statistics and Projections: Long-term baseline projections, 103. Retrieved from: https://data.oecd.org/gdp/real-gdp-long-term-forecast.htm#indicator-chart .
• OPEC. (2022). Members countries facts and figures. Retrieved from: https://www.opec.org/opec_web/en/about_us/25.htm .
• Ortega, L., & Khashanah, K. (2014). A Neuro wavelet model for the Short-Term forecasting of High Frequency time series of stock returns. Journal of Forecasting, 33(2), pp. 134-146, doi:10.1002/for.2270
• Prochazka, A., Mudrova, M., Vysata, O., & Araujo, C.P.S. (2010). Multi-Channel EEG Signal Segmentation and Feature Extraction. Proceedings of Intelligent Engineering Systems (INES), doi: 10.1109/INES.2010.5483824
• Renaud, O., Starck, J.L., & Murtagh, F. (2002). Wavelet-based Forecasting Short and Long Memory Time Series. Cahiers du departement d’econometrie, Universite de Geneve, 4.
• Riedel, B. (2020). As a global economic crisis wreaks havoc on Saudi Arabia, the kingdom should reduce military spending”, Brookings, Retrieved from: https://www.brookings.edu/blog/order-from-chaos/2020/05/27/as-a-global-ecomic-crisis-wreaks-havoc-on-saudi-arabia-the-kingdom-should-reduce-military-spending/ .
• Rostan, P., Belhachemi, R., & Rostan, A. (2015). Appraising the financial sustainability of a pension system with signal processing”, Studies of Applied Economics, 33(3), 801-816, doi: https://doi.org/10.25115/eea.v33i3.3134, Retrieved from: https://ojs.ual.es/ojs/index.php/eea/article/view/3134 .
• Rostan, P., Belhachemi, R., & Racicot, F.E. (2017). Forecasting the yield curve with the Burg model. Journal of Forecasting, 36(1), 91-99, doi: https://doi.org/10.1002/for.2416, Retrieved from: https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2416 .
• Rostan, P., & Rostan, A. (2017). Population Projections and Pension System Sustainability. Saarbrücken, Germany: Lambert Academic Publishing. ISBN978-620-2-06479-8
• Rostan, P., & Rostan, A. (2018a). The versatility of spectrum analysis for forecasting financial time series. Journal of Forecasting, 37(3), 327-339, doi: https://doi.org/10.1002/for.2504, Retrieved from: http://onlinelibrary.wiley.com/doi/10.1002/for.2504/abstract .
• Rostan, P., & Rostan, A. (2018b). Will Saudi Arabia Get Older? Will its pension system be sustainable? Spectral Answers. PSU Research Review, 2(3), doi: https://doi.org/10.1108/PRR-12-2017-0045, Retrieved from: https://www.emeraldinsight.com/doi/full/10.1108/PRR-12-2017-0045 .
• Rostan, P., & Rostan, A. (2018c). Forecasting Spanish nominal and real GDPs with Spectral Analysis”, Studies of Applied Economics, 36(1), 217-234, Retrieved from: https://dialnet.unirioja.es/servlet/articulo?codigo=6283924 .
• Rostan, P., & Rostan, A. (2018d). Where is Greek’s Economy Heading? International Journal of Management and Applied Science (IJMAS), 4(3), 28-31, Retrieved from: http://ijmas.iraj.in/paper_detail.php?paper_id=11490andname=Where_is_Greece%E2%80%99s_Ecomy_Heading?_A_Spectral_Perspective .
• Rostan, P., & Rostan, A. (2019). When will European Muslim Population be majority and in which country. PSU Research Review, 3(2), doi: https://doi.org/10.1108/PRR-12-2018-0034,
• Retrieved from: https://www.emerald.com/insight/content/doi/10.1108/PRR-12-2018-0034/full/html .
• Rostan, P., & Rostan, A. (2020). Where is Austria’s Economy Heading? Economic and Business Review, 22(1), 105-130, doi: https://doi.org/10.15458/ebr97, Retrieved from: https://www.ebrjournal.net/uploads/ebr/public/document/13-ebr_221_d_rostan_barvni_en.pdf .
• Rostan, P., & Rostan, A. (2021a). Where is Saudi Arabia’s Economy Heading?, International Journal of Emerging Markets, 16(8), 2009-2033. doi: https://doi.org/10.1108/IJOEM-08-2018-0447).
• Rostan, P. & Rostan A. (2021b). Where are fossil fuels prices heading?, International Journal of Energy Sector Management, 15(2), 309-327. doi: https://doi.org/10.1108/IJESM- 07-2019-0009.
• Rostan, P., & Rostan, A. (2022a). 2050 Projections of the Persian Gulf Economies, Iranian Economic Review, 26(2), 269-288. doi: 10.22059/ier.2022.88164. https://ier.ut.ac.ir/article_88164.html
• Rostan, P. & Rostan A. (2022b). Assessing the Resilience of Turkey’s Economy during the Covid-19 Pandemic with its 2050 Projections, Journal of Emerging Economies & Policy, 7(2), 38-49. https://dergipark.org.tr/en/download/article-file/2595010
• Rostan, P. & Rostan A. (2022c). Assessing the Resilience of UK’s Economy After The Covid-19 Pandemic And Brexit, Online Journal Modelling The New Europe, 40, 47-77. (doi: 10.24193/OJMNE.2022.40.03) http://neweurope.centre.ubbcluj.ro/wp-content/uploads/2022/12/3.pdf
• Rostan, P. & Rostan A. (2023a). How South Korea’s economy gained momentum because of Covid-19, Studies of Applied Economics, article in press.
• Schlüter, S., & Deuschle, C. (2010). Using wavelets for time series forecasting: Does it pay off? Working Paper, IWQW discussion paper series, in Cooperation with: Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, 4.
• Soyemi, A., Ore-Oluwa, R., & Akingula, J.O. (2017). Effects of oil price shock on stock returns of energy firms in Nigeria. Kasetsart Journal of Social Sciences, ISSN 2452-3151, Retrieved from: https://doi.org/10.1016/j.kjss.2017.09.004. .
• Statista, (2022). Share of the gross domestic product generated from oil rents in the Gulf Cooperation Council in 2019, by country. Retrieved from: https://www.statista.com/statistics/1303697/gcc-oil-rent-share-of-gdp-by-country/ .
• Stoica, P., & Moses, R. (2005). Spectral Analysis of Signals, Prentice Hall, Upper Saddle River.
• Tan, Z., Zhang, J., Wang, J., & Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Applied Energy, 87(11), 3606-3610, doi:10.1016/j.apenergy.2010.05.012
• The Global Economy. (2022a). Revenue minus production cost of oil, percent of GDP in Iran. Retrieved from: https://www.theglobalecomy.com/Iran/oil_revenue/ .
• The Global Economy. (2022b) Revenue minus production cost of oil, percent of GDP in Saudi Arabia. Retrieved from: https://www.theglobalecomy.com/Saudi-Arabia/ .
• Valens, C. (1999). A Really Friendly Guide to Wavelets, Retrieved from: http://agl.cs.unm.edu/~williams/cs530/arfgtw.pdf .
• Wallace, P., & Martin, M. (2021). Saudi Aramco Follows Big Oil Competitors as Profit Surges. Bloomberg. Retrieved from: https://www.bloombergquint.com/business/saudi-aramco-follows-big-oil-rivals-with-a-profit-surge .
• World Bank. (2023). Real GDP at Constant National Prices for Saudi Arabia and Iran, Millions of 2017 U.S. Dollars, Annual, not Seasonally Adjusted. Retrieved from: https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=SA
• https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=IR
• World Bank. (2023). GDP (current US$). Retrieved from: https://data.worldbank.org/indicator/Ny.Gdp.Mktp.Cd?most_recent_value_desc=true .