Dr. Ahamuefula Ephraim Ogbonna holds a Professional Diploma, a Bachelor’s Degree and a Master’s Degree in Statistics, from the University of Ibadan, and has in the process, garnered the pre-requisite knowledge in the rudiments of Statistics, both theoretical and empirical. He is a Research Fellow at the Centre for Econometrics and Applied Research (CEAR), Ibadan, Nigeria; with over seven (7) years research experience, working with renowned Statisticians and Economists. Under the CEAR platform, which he joined in 2015, he has participated in the macroeconomic modelling of the Nigerian economy that provides evidence-based policy options to the Monetary Policy Committee (MPC) of the Central Bank of Nigeria (CBN), to aid their policy decisions; and also co-facilitated trainings of post-graduate students, academics and professionals in the use of Econometric tools and statistical soft-wares for empirical research analyses. He has also been actively involved in the Project LINK Meeting for the period between 2015 and 2018; reporting Nigeria’s economic outlook annually, having empirically examined and forecasted key macroeconomic fundamentals for the years under review. As a young researcher, his research focuses on empirical application of recent Econometric methodologies. He has interest in programming with diverse software programs, to solve analytical and numeric problems, with demonstrated proficiency in the usage of statistical programming soft-wares. He has several publications in ISI and Scopus indexed journals. As at October 2023, he is ranked in the top 3% (Nigeria) and 8% (Africa), according to RePEC/IDEAS ranking of economists based on his economic related publications.
B.Sc , M.Sc & PhD
- Energy modeling
- Spillovers and Financial Connectedness
- Commodity Economics
- Financial modeling
- Journal Publications
1. A.A. Salisu, A.E. Ogbonna and T.F. Oloko (2022). Pandemics and cryptocurrencies. IIMB Management Review (To appear)
2. A.A. Salisu, R. Gupta and A.E. Ogbonna (2022). A Moving Average Heterogeneous Autoregressive Model for Forecasting the Realized Volatility of the US Stock Market: Evidence from Over a Century of Data. International Journal of Finance and Economics, 27(1), 384 –400. https://doi.org/10.1002/ijfe.2158
3. T.F. Oloko, A.E. Ogbonna, A.A. Adedeji and N. Lakhani (2021). Fractional cointegration between gold price and inflation rate: Implication for inflation rate persistence. Resources Policy, 74, 102369.
4. A.A. Salisu and A.E. Ogbonna (2021). The return volatility of crypto currencies during the COVID-19 pandemic: Assessing the news effect. Global Finance Journal, 100641.
5. O.G. Awolaja, O.S. Yaya, A.E. Ogbonna, S.O. Joseph and X.V. Vo (2021). Unemployment hysteresis in Middle East and North Africa countries: panel SUR-based unit root test with a Fourier function. Middle East Development Journal, 1-17.
6. A.A. Salisu, R. Gupta and A.E. Ogbonna (2021). Point and density forecasting of macroeconomic and financial uncertainties of the USA. Journal of Forecasting, 40: 700–707. https://doi.org/10.1002/for.2740
7. A.E. Ogbonna and O.E. Olubusoye (2021). Tail Risks and Stock Return Predictability: Evidence From Asia-Pacific. Asian Economics Letters, 2(3), 24417.
8. O.E. Olubusoye, O.J. Akintande, O.S. Yaya, A.E. Ogbonna, and A.F. Adenikinju (2021). Energy pricing during the COVID-19 pandemic: Predictive information-based
uncertainty indexes with machine learning algorithm. Intelligent Systems with Applications,12, 200050. https://doi.org/10.1016/j.iswa.2021.200050
9. T.F. Oloko, A.E. Ogbonna, A.A. Adedeji and N. Lakhani (2021). Oil price shocks and inflation rate persistence: A Fractional Cointegration VAR approach. Economic Analysis and Policy, 70, 259-275.
10. A.A. Salisu, A.E. Ogbonna, T.F. Oloko and I.A. Adediran (2021). A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic. Sustainability, 13(6), 3212.
11. O.E. Olubusoye, A.E. Ogbonna, O.S. Yaya and D. Umolo (2021). An Information-Based Index of Uncertainty and the predictability of Energy Prices. International Journal of Energy Research, https://doi:10.1002/er.6512
12. O.S. Yaya, A.E. Ogbonna, F. Furuoka and L.A. Gil-Alana (2021). A new unit root analysis for testing hysteresis in unemployment. Oxford Bulletin of Economics and Statistics, https://doi:10.1111/obes.12422
13. O.S. Yaya, O.A. Otekunrin and A.E. Ogbonna (2021). Life Expectancy in West African countries, Evidence of Convergence and Catching up with the North. Statistics in Transition, 22(1), 75-89.
14. O.S. Yaya, A.E. Ogbonna, Mudida, R. and Abu, N. (2021). Market Efficiency and Volatility Persistence of Cryptocurrency during Pre- and Post-Crash Periods of Bitcoin: Evidence based on Fractional Integration. International Journal of Finance and Economics, 26, 1318–1335. https://doi.org/10.1002/ijfe.1851
15. A.A. Salisu, A.E. Ogbonna and I. Adediran (2021). Stock-Induced Google Trends and the predictability of Sectoral Stock Returns. Journal of Forecasting, 40: 327- 345 https://doi.org/10.1002/for.2722
16. O.S. Yaya, X.V. Vo, A.E. Ogbonna and A. Adewuyi (2020). Modelling Cryptocurrency High Low Prices using Fractional Cointegrating VAR. International Journal of Finance and Economics, https://doi.org/10.1002/ijfe.2164
17. L.A. Gil-Alana, R. Mudida, O.S. Yaya, K.A. Osuolale and A.E. Ogbonna (2020). Mapping US Presidential terms with S&P500 Index: Time Series Analysis Approach. International Journal of Finance and Economics, 26(2) 1938 – 1954 https://doi.org/10.1002/ijfe.1887
18. S. Olofin, T. F. Oloko, K. O Isah and A. E. Ogbonna (2020). Crude oil price–shale oil production nexus: a predictability analysis. International Journal of Energy Sector Management, https://doi.org/10.1108/IJESM-05-2019-0004
19. A.A. Salisu, A.E. Ogbonna and A. Adewuyi (2020). Google trends and the predictability of precious metals. Resources Policy 65, https://doi.org/10.1016/j.resourpol.2019.101542
20. O.S. Yaya, A.E. Ogbonna and Mudida, R. (2019). Hysteresis of Unemployment rate in Africa: New Findings from Fourier ADF test. Quality and Quantity, 53(4), https://doi:10.1007/s11135-019-00894-6
21. O.S. Yaya, A.E. Ogbonna and N.V. Atoi (2019). Are inflation rates in OECD countries actually stationary during 2011-2018? Evidence based on Fourier Nonlinear Unit root tests with Break. Empirical Economics Review, 9(4), 309 – 325.
22. O.S. Yaya, A.E. Ogbonna and O.E. Olubusoye (2019). How Persistent and Dynamic Inter Dependent are pricing Bitcoin to other Cryptocurrencies Before and After 2017/18 Crash? Physica A, Statistical Mechanics and Applications. https://doi.org/10.1016/j.physa.2019.121732
23. O.S. Yaya, O.J. Akintande, A.E. Ogbonna and M.A. Hammed (2019). CPI inflation in Africa: Fractional persistence, Mean reversion and Nonlinearity. Statistics in Transition new series, 20(3), 119 – 132. https://doi.org/10.21307/stattrans-2019-027
24. A.A. Salisu and A.E. Ogbonna (2019). Another look at the energy-growth nexus: New insights from MIDAS regressions. Energy (2019), https://doi:10.1016/j.energy. 2019.02.138
25. O.S. Yaya, S. Luqman, D.M. Akinlana, M.M. Tumala, A.E. Ogbonna (2017): Oil Price-US Dollars Exchange Returns and Volatility Spillovers in OPEC Member Countries: Post Global Crisis Period’s Analysis. African Journal of Applied Statistics, 4(1): 191-208.
26. O.S. Yaya, D.M. Akinlana, A.E. Ogbonna (2017). Investigating Structural break-GARCH based Unit root test in US exchange rates. Journal of Science Research, 16.
1. O.E. Olubusoye and A.E. Ogbonna (2020). COVID-19 and the Nigeria Economy: Analyses of Impacts and Growth Projections. Centre for Petroleum Energy Economics and Law [CPEEL] COVID-19 Discussion Paper Series, https://www.researchgate.net/publication/342439011
2. O.E. Olubusoye, S.O. Olofin, S. Alade, T.F. Oloko, A.E. Ogbonna, K.O. Isah (2016). Forecasting the Impact of Global Oil Price Movement on the Nigerian Economy. The Quest for Development: Essays in Honour of Professor Akin Iwayemi.
3. O.E Olubusoye and A.E. Ogbonna (2015). On Model Selection Criteria: An Evaluation of Bayesian Model Averaging Approach Using Monte Carlo Simulation. Perspectives and Developments in Mathematics: Proceedings of Conference in Honour of Professor S. A. Ilori,pp. 191- 210.
1. L. A. Gil-Alana, R. Mudida, O.S. Yaya, K. A. Osuolale and A.E. Ogbonna (2019). Mapping US Presidential Terms with S&P500 Index: Time Series Analysis Approach. Paper Presented at the 21st Multidisciplinary International Sciences, Technology, Education, Arts, Management and Social Sciences (ISTEAMS) conference, CSIR-INSI, Cantonment and Balme Conference Hall, University of Ghana, Accra, Ghana. November 14- 16, 2019.
2. O.S. Yaya and A.E. Ogbonna (2019). Modelling Crude Oil-Petroleum Products’ Price Nexus using Dynamic Conditional Correlation GARCH models. Proceedings of the 12th Annual NAEE/IAEE Conference. To appear.
3. S.O. Olofin, Oloko, T. F., Isah, K. O. and A.E. Ogbonna (2019). Crude oil Price – Shale Production Nexus: A predictability Analysis. Proceedings of the 12th Annual NAEE/IAEE Conference. To appear.
4. O.E. Olubusoye, T.F. Oloko, K.O. Isah and A.E. Ogbonna (2015). Impact of oil price and monetary policy shocks on macroeconomic fundamentals: Evidence from Nigeria. The Nigerian Economic Society (NES), 56th Annual Conference Proceedings, Ladi Kwali Hall, Sheraton Hotel, Abuja.
5. O.E Olubusoye and A.E. Ogbonna (2015). A Simple Application of the Gibbs Sampling Algorithm in the Normal Linear Regression Model. Paper presentation at 39th Annual National Conference of the Nigerian Statistical Association (NSA).
6. O.E Olubusoye and A.E. Ogbonna (2015). On Model Selection Criteria: An Evaluation of Bayesian Model Averaging Approach Using Monte Carlo Simulation. Perspectives and Developments in Mathematics: Proceedings of Conference in Honour of Professor S. A. Ilori, pp. 191- 210.
7. O.E Olubusoye and A.E. Ogbonna (2015): On Model Selection Criteria: An Evaluation of Bayesian Model Averaging Approach Using Monte Carlo Simulation. Faculty of Science University of Ibadan, Nigeria 2nd International Conference on Scientific Research and Innovation in Nigeria.
8. O.E Olubusoye and A.E. Ogbonna (2014). Modelling Inflation Process in Nigeria using Bayesian Model Averaging. Second Bayesian Young Statisticians Meeting (BAYSM 2014), Vienna, Austria; DOI:10.13140/2.1.2439.6805
9. O.E. Olubusoye, O.S. Yaya and A.E. Ogbonna (2014). Modelling Nigerian Electricity Demand using Structural Time Series Approach. Proceedings of the 7th Annual NAEE/IAEE Conference on Energy Access for Economic Development: Policy, Institutional Frameworks and Strategic Options, edited by Adenikinju, A., Iwayemi, A. and Iledare, W. Chapter 37: 664-677.
Working Papers/Papers under Review
1. A.A. Salisu, A.E. Ogbonna and P.A. Omosebi (2021). Revisiting the role of estimators in forecasting: New insights. Submitted to: Communications in Mathematics and Applications.
2. A.A. Salisu, R. Gupta and A.E. Ogbonna (2021). Tail Risks and Forecast ability of Stock Returns of Advanced Economies: Evidence from Centuries of Data (No. 202117).
3. A.E. Ogbonna and O.E. Olubusoye (2019). Monetary Policy Transmission Mechanism: A Classical and Bayesian Perspective.
4. O.E. Olubusoye and A.E. Ogbonna (2018). Model Averaging in Econometric Modelling: Review of Recent Developments.
5. A.A. Salisu and A.E. Ogbonna (2018). Does time-variation matter in the stochastic volatility components for G7 stock returns. Working Papers 062, Centre for Econometric and Allied Research, University of Ibadan.
6. A.A. Salisu, L.O. Akanni and A.E. Ogbonna (2018). Forecasting CO2 emissions: Does the choice of estimator matter? Working Papers 045, Centre for Econometric and Allied Research,University of Ibadan.
7. A.A. Salisu and A.E. Ogbonna (2017). Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models. Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
8. A.A. Salisu and A.E. Ogbonna (2017). Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach. Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.