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Time series analysis of economic growth rate series in Nigeria: structural breaks, non-linearities and reasons behind the recent recession

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  • Olushina Olawale Awe
  • Luis Alberiko Gil-Alana

Abstract

This article deals with the modelling of growth rate time series in Nigeria with a view of detecting its statistical properties, structural breaks and non-linearities. We employ both fractional integration and structural break time series techniques in modelling the annual growth rate series of the Nigerian GDP growth rate for about 55 years. The data span between 1960 and 2017. The results show that Nigerian growth rate is unstable with non-linearities and long-range dependence structures. We also investigate what might explain these features and conclude that erratic political institutions, associated with poor economic management and insecurity in Nigeria, among others, in the decades after independence are the root causes of non-linearities observed, which have also led to the subsequent recent economic recession in Nigeria.

Suggested Citation

  • Olushina Olawale Awe & Luis Alberiko Gil-Alana, 2019. "Time series analysis of economic growth rate series in Nigeria: structural breaks, non-linearities and reasons behind the recent recession," Applied Economics, Taylor & Francis Journals, vol. 51(50), pages 5482-5489, October.
  • Handle: RePEc:taf:applec:v:51:y:2019:i:50:p:5482-5489
    DOI: 10.1080/00036846.2019.1613513
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    Cited by:

    1. Joseph Ifeolu Falegan & Ebele Amali, 2023. "An Evaluation of the Optimal Inflation Target for Economic Growth in Nigeria," Journal of Social Science Studies, Macrothink Institute, vol. 10(1), pages 1-1, June.
    2. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    3. Awe, Olushina Olawale & Musa, Ann Precious & Sanusi, Gbenga Peter, 2023. "Revisiting economic diversification in Africa's largest resource-rich nation: Empirical insights from unsupervised machine learning," Resources Policy, Elsevier, vol. 82(C).
    4. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.

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