Main Article Content
Abstract
On the rise in Premium Motor Spirit (PMS) prices and cash rates, a conventional least squares analysis was used to determine the relationship between the respondent variable, inflation, and the explanatory variables, PMS price and exchange rate. According to the results, as evidenced by the conventional least squares regression, the PMS price and money rate were significant drivers of inflation, accounting for about 88% of the fluctuation in inflation. Additionally, the Breusch-agnostic test revealed that the residuals of the direct regression model were not heteroscedastic, and the ACF and PACF tests revealed that the error terms did not have autocorrelation. The Jarque-Bera ordinariness test was used to express the perceived background noise as normal. As demonstrated by the findings, the increase in the price of PMS and the decline in the value of the Naira influenced Nigerian inflation. Finally, based on the research econometric outcomes and interpretations, the study discussed the policy implications of these findings and offered recommendations. For future work, research should be conducted on energy transition and efficiency.
Keywords
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References
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References
C.K.-S Leung, Q.I. Khan, Z. Li, and T. Hoque, “CanTree: A Canonical-Order Tree for Incremental Frequent-Pattern Mining,” Knowledge and Information Systems, Vol. 11, No. 3, pp. 287–311, Ap. 2007, doi: 10.1007/s10115-006-0032-8.
D. Umoru and O.P. Odjegba, “Exchange Rate Misalignment and Balance of Payment Adjustment in Nigeria,” European Scientific Journal, Vol. 9, No. 13, pp. 260–273, May 2013.
S.O. Oyedepo, “Towards Achieving Energy for Sustainable Development in Nigeria,” Renewable and Sustainable Energy Reviews, Vol. 34, pp. 255–272, Jun. 2014, doi: 10.1016/j.rser.2014.03.019.
B. Katoka and J.M. Dostal, “Natural Resources, International Commodity Prices and Economic Performance in Sub-Saharan Africa (1990–2019),” Journal of African Economies, Vol. 31, No. 1, pp. 53–74, Jan. 2022, doi: 10.1093/jae/ejab014.
B. Eregha, E. Mesagan, and O. Ayoola, “Petroleum Products Prices and Inflationary Dynamics in Nigeria,” The Empirical Econometrics and Quantitative Economics Letters, Vol. 4, No. 4, pp. 108–122, Dec. 2015.
G.O. Odularu, “Crude oil and the Nigerian economic performance,” Oil and Gas business, 2008. [Online]. Available: https://d1wqtxts1xzle7.cloudfront.net/5921722/odularo_1-libre.pdf
A. Iwayemi and A. Adenikinju, “Macro-Economic Implications of Higher Energy Prices in Nigeria,” Pacific and Asian Journal of Energy, Vol. 6, No. 1, pp. 13–24, 1996.
T.H. Le and Y. Chang, “Oil Price Shocks and Trade Imbalances,” Energy Economics, Vol. 36, pp. 78–96, Mar. 2013, doi: 10.1016/j.eneco.2012.12.002.
A. Berg, S. O’Connell, C. Pattillo, R. Portillo, and F. Unsal, “Monetary Policy Issues in Sub-Saharan Africa,” in Oxford Handbook of Africa and Economics: Volume 2: Policies and Practices, C. Monga and J.Y. Lin, Eds., Oxford, England: Oxford University Press, 2014, pp. 62–87.
M.B. Laurens, K. Eckhold, D. King, M.N.Ø. Mæhle, A. Naseer, and A. Durré, “The Journey to Inflation Targeting: Easier Said Than Done the Case for Transitional Arrangements Along the Road,” International Monetary Fund, Washington, D.C., USA, WP/15/136, Jun. 2015.
M. Abatcha, “Empirical Analysis of Oil price changes on Inflation in Nigeria,” Journal of Management and Economic Studies, Vol. 3, No, 3, pp. 84–101, 2021, doi: 10.26677/TR1010.2022.915.
P.A. Olomola and A.V. Adejumo, “Oil Price Shock and Macroeconomic Activities in Nigeria,” International Research Journal of Finance and Economics, No. 3, pp. 28–34, May 2006.
T.S. Shitile and N. Usman, “Disaggregated Inflation and Asymmetric Oil Price Pass‐Through in Nigeria,” International Journal of Energy Economics and Policy, Vol. 10, No. 1, pp. 255–264, 2019, doi: 10.32479/ijeep.8343.
M.N. Uwakonye, G.S. Osho, and H. Anucha, “The Impact of Oil and Gas Production on the Nigerian Economy: A Rural Sector Econometric Model,” International Business & Economics Research Journal (IBER), Vol. 5, No. 2, pp. 61–76, Feb. 2006, doi: 10.19030/iber.v5i2.3458.
C.P. Nwosu. “Import of Fuel Prices on Inflation: Evidence from Nigeria. Research Department, Central Bank of Nigeria.” ResearchGate. Accessed: Feb. 13, 2022. [Online.] Available: https://www.researchgate.net/publication/228304975_Impact_of_Fuel_Price_on_Inflation_Evidences_from_Nigeria
P.E. Arinze, “The Impact of Oil Price on the Nigerian Economy,” Journal of Research in National Development, Vol. 9, No. 1, pp. 211–215, Aug. 2013.
U. Ologe, N.I. Erameh, V. Ojakorotu, and N.A. Tshidzumba, “An Analysis of State-Citizens Relations during the COVID-19 Pandemic Era in Africa: Nigeria and South Africa in Comparative Perspective,” in Proceedings on the Conference on the Implications of Covid-19 on Gender and Behaviour in Africa, 2021, pp. 50–60.
S. Solaymani, R. Kardooni, F. Kari, and S.B. Yusoff, “Economic and Environmental Impacts of Energy Subsidy Reform and Oil Price Shock on the Malaysian Transport Sector,” Travel Behaviour and Society, Vol. 2, No. 2, pp. 65–77, May 2015, doi: 10.1016/j.tbs.2014.09.001.
M. Cicowiez, O. Akinyemi, T. Sesan, O. Adu, and B. Sokeye, “Gender-Differentiated Impacts of a Rural Electrification Policy in Nigeria,” Energy Policy, Vol. 162, pp. 1–14, Mar. 2022, Art. no. 112774, doi: 10.1016/j.enpol.2021.112774.
J.D. Millington, G.L. Perry, and R. Romero-Calcerrada, “Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape,” Ecosystems, Vol. 10, No. 4, pp. 562–578, Jun. 2007, doi: 10.1007/s10021-007-9020-4.
S.K. Sharma, A.H. Al-Badi, S.M. Govindaluri, M.H. Al-Kharusi, “Predicting Motivators of Cloud Computing Adoption: A Developing Country Perspective,” Computers in Human Behavior, Vol. 62, pp. 61–69, Sep. 2016, doi: 10.1016/j.chb.2016.03.073.
O. Harel, “The Estimation of R2 and Adjusted R2 In Incomplete Data Sets Using Multiple Imputation,” Journal of Applied Statistics, Vol. 36, No. 10, pp. 1109–1118, Sep. 2009, doi: 10.1080/02664760802553000.
I.U. Moffat and E.A. Akpan, “White Noise Analysis: A Measure of Time Series Model Adequacy,” Applied Mathematics, Vol. 10, No. 11, pp. 989–1003, Nov. 2019, doi: 10.4236/am.2019.1011069.
E.A. Akpan and I.U. Moffat, “Modeling the Autocorrelated Errors in Time Series Regression: A Generalized Least Squares Approach,” Journal of Advances in Mathematics and Computer Science, Vol. 26, No. 4, pp. 1–15, 20 18, doi: 10.9734/JAMCS/2018/39949.
Ö.G. Alma, “Comparison of Robust Regression Methods in Linear Regression,” International Journal of Contemporary Mathematical Sciences, Vol. 6, No. 9, pp. 409–421, 2011.
W.W.S. Wei, “Time Series Analysis,” in The Oxford Handbook of Quantitative Methods in Psychology, Vol. 2, Statistical Analysis, T.D. Little, Ed., Oxford, England: Oxford University Press, 2013, Ch. 22, pp. 458–485.
J. Mourtada, T. Vaškevičius, and N. Zhivotovskiy, “Distribution-Free Robust Linear Regression,” Mathematical Statistics and Learning, Vol. 4, pp. 253–292, 2021, doi: 10.4171/MSL/27.
E.H. Green, Econometric Analysis, 5th ed. Hoboken, NJ, USA: Prentice Hall, 2002.
N.D. Bennett et al., “Characterising Performance of Environmental Models,” Environmental Modelling & Software, Vol. 40, pp. 1–20, Feb. 2013, doi: 10.1016/j.envsoft.2012.09.011.
K. Yurekli, A. Kurunc, and F. Ozturk, “Application of Linear Stochastic Models to Monthly Flow Data of Kelkit Stream,” Ecological Modelling, Vol. 183, No. 1, pp. 67–75, Apr. 2005, doi: 10.1016/j.ecolmodel.2004.08.001.
J.O. Rawlings, S.G. Pantula, and D.A. Dickey, Eds. Applied Regression Analysis: A Research Tool. New York, NY, USA: Springer New York, 1998.
D. Liu, and H. Zhang, “Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach,” Journal of the American Statistical Association, Vol. 113, No. 522, pp. 845–854, 2018, doi: 10.1080/01621459.2017.1292915.
L. Denby and C. Mallows, “Two Diagnostic Displays for Robust Regression Analysis,” Technometrics, Vol. 19, No. 1, pp. 1–13, 1977, doi: 10.1080/00401706.1977.10489492.
K. Bhaskaran, A. Gasparrini, S. Hajat, L. Smeeth, and B. Armstrong, “Time Series Regression Studies in Environmental Epidemiology,” International Journal of Epidemiology, Vol. 42, No. 4, pp. 1187–1195, 2013, doi: 10.1093/ije/dyt092.