Forecasting China-Africa economic integration using Wavelet-ARIMA hybrid approach

Marvellous Ngundu, Reon Matemane

Article ID: 2647
Vol 7, Issue 3, 2023

VIEWS - 357 (Abstract) 131 (PDF)

Abstract


China-Africa economic integration generally looks lucid, as evidenced by rising bilateral trade, as well as Chinese FDI, aid, and debt financing for infrastructure development in Africa. The engagement, however, appears to be strategically channeled to benefit China’s resource endowment strategy. First, Chinese FDI in Africa is primarily resource-seeking, with minimum manufacturing value addition. Second, China has successfully replicated the Angola model in other resource-rich African countries, and most infrastructure loans-for-natural resources barter deals are said to be undervalued. There is also a resource-backed loan arrangement in place, in which default Chinese loans are repaid in natural resources. Third, while China claims that its financial aid is critical to Africa’s growth and development processes, a significant portion of the aid is spent on non-development projects such as building parliaments and government buildings. This lend credence to the notion that China uses aid to gain diplomatic recognition from African leaders, with resource-rich and/or institutionally unstable countries being the most targeted. The preceding arguments support why Africa’s exports to China dominate other China’s financial flows to Africa, and consist mainly of natural resources. Accordingly, this study aims to forecast China-Africa economic integration through the lens of China’s demand for natural resources and Africa’s demand for capital, both of which are reflected in Africa’s exports to China. The study used a MODWT-ARIMA hybrid forecasting technique to account for the short period of available China-Africa bilateral trade dataset (1992–2021), and found that Africa’s exports to China are likely to decline from US$ 119.20 billion in 2022 to US$ 13.68 billion in 2026 on average. This finding coincides with a period in which Chinese demand for Africa’s natural resources is expected to decline.


Keywords


Africa’s exports to China; autoregressive integrated moving average (ARIMA); China-Africa economic integration; maximal overlap discrete wavelet transform (MODWT)

Full Text:

PDF


References


Abesadze N (2017). How to estimate the degree of economic integration on the basis of statistical methods. Intellectual Property Rights: Open Access. doi: 10.4172/2375-4516.1000190

Arribas I, Pérez F, Tortosa-Ausina E (2007). Measuring international economic integration: Theory and evidence of globalization. Available online: https://mpra.ub.uni-muenchen.de/16010/ (accessed on 12 December 2022).

Begu LS, Vasilescu MD, Stanila L, Clodnitchi R (2018). China-Angola investment model. Sustainability 10(8): 2936. doi: 10.3390/su10082936

CARI (2021). China-Africa bilateral trade, investment and loan data. Available online: http://www.sais-cari.org/data-china-africa-trade (accessed on 8 September 2021).

Chakraborty T, Ghosh I (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals 135: 109850. doi: 10.1016/j.chaos.2020.109850

Cissé D (2013). Forum: China’s engagement in Africa: Opportunities and challenges for Africa. African East-Asian Affairs. doi: 10.7552/0-4-116

Conejo AJ, Plazas MA, Espinola R, Molina AB (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

Dghais A, Ismail M (2013). A comparative study between discrete wavelet transform and maximal overlap discrete wavelet transform for testing stationarity. World Academy of Science, Engineering and Technology International Journal of Mathematical Science and Engineering 7(12): 471–475.

Gurumoorthy S, Muppalaneni NB, Kumari GS (2020). EEG Signal denoising using Haar Transform and Maximal Overlap Discrete Wavelet Transform (MODWT) for the finding of epilepsy. In: Epilepsy-Update on Classification, Etiologies, Instrumental Diagnosis and Treatment Overlap. IntechOpen.

Haifang L (2017). China’s influence in Africa: Current roles and future prospects in resource extraction. Journal of Sustainable Development Law and Policy (The) 8(1): 34–59. doi: 10.4314/jsdlp.v8i1.3

Hyndman RJ, Khandakar Y (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software 27(1): 1–22. doi: 10.18637/jss.v027.i03

Hyndman RJ, Athanasopoulos G (2018). Forecasting: Principles and Practice, 2nd ed. OTexts.

Joo TW, Kim SB (2015). Time series forecasting based on wavelet filtering. Expert Systems with Applications 42(8): 3868–3874. doi: 10.1016/j.eswa.2015.01.026

Jureńczyk Ł (2020). Analysing China’s “Angola Model”: A pattern for chinese involvement in Africa? Strategic Review for Southern Africa 42(2): 43–61. doi: 10.35293/srsa.v42i2.73

Machado P (2021). Assessing China and Angola relations: The implications of the ‘Angola model’ of economic development. Available online: https://iep.lisboa.ucp.pt/asset/8161/file (accessed on 12 December 2022).

Ngundu M (2022). How economic growth in Africa responds to Chinese loans: Evidence from new CARI’s loan dataset. In: Osabuohien E, Odularu G, Ufua D, Osabohien R (editors). COVID-19 in the African Continent. Emerald Publishing Limited. pp. 183–199. doi: 10.1108/978-1-80117-686-620221015

Ngundu M, Ngalawa H (2023). Causal relationship between Africa’s growth and chinese debt financing for infrastructure development. Montenegrin Journal of Economics 19(1): 127–137. doi: 10.14254/1800-5845/2023.19-1.11

Nguyen T, He TX (2015). Wavelet analysis and applications in economics and finance. Research & Reviews : Journal of of Statistics and Mathematical Sciences 22–37.

Nury AH, Hasan K, Alam MJB (2017). Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. Journal of King Saud University - Science 29(1): 47–61. doi: 10.1016/j.jksus.2015.12.002

Paul RK, Ghosh H (2013). Wavelet frequency domain Approach for modelling and forecasting of Indian monsoon rainfall time-series data. Journal of the Indian Society of Agricultural Society 67(3): 319–327.

Pigato M, Tang W (2015). China and Africa: Expanding economic ties in an evolving global context. Working Paper No. 95161. World Bank Group. Available online: http://documents.worldbank.org/curated/en/241321468024314010/China-and-Africa-expanding-economic-ties-in-an-evolving-global-context (accessed on 8 September 2021).

Prabhakar AC, Erokhin V, Godara RS (2020). Economic integration of African economies with China and India. In: Regional Trade and Development Strategies in the Era of Globalization. IGI Global. pp. 25–48.

Rhif M, Ben Abbes A, Farah IR, et al. (2019). Wavelet transform application for/in non-stationary time-series analysis: A review. Applied Sciences 9(7): 1345. doi: 10.3390/app9071345

Schiere R (2011). China and Africa: An emerging partnership for development? Working Paper No. 125. African Development Bank Group. Available online: https://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/WPS%20No%20125%20China%20and%20Africa%20%20An%20Emerging%20Partnership%20.pdf (accessed on 8 September 2021).

Schlüter S, Deuschle C (2010). Using wavelets for time series forecasting: Does it pay off? IWQW Discussion Papers, No. 04/2010. Available online: https://www.econstor.eu/handle/10419/36698 (accessed on 8 September 2021).

Sun Y (2021). FOCAC 2021: China’s retrenchment from Africa? Available online: https://www.brookings.edu/blog/africa-in-focus/2021/12/06/focac-2021-chinas-retrenchment-from-africa/ (accessed on 12 December 2022).

Were A (2018). Debt trap? Chinese loans and Africa’s development options. Available online: https://www.jstor.org/stable/pdf/resrep25988.pdf (accessed on 12 December 2022).




DOI: https://doi.org/10.24294/jipd.v7i3.2647

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Marvellous Ngundu, Aaron Tesfaye

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This site is licensed under a Creative Commons Attribution 4.0 International License.