IDEAS home Printed from https://ideas.repec.org/a/spr/snbeco/v5y2025i4d10.1007_s43546-025-00797-9.html
   My bibliography  Save this article

Modelling other depository corporations’ sectoral credit and economic growth nexus in Nigeria: a panel ARDL approach

Author

Listed:
  • M. O. Adenomon

    (Nasarawa State University)

  • Hamman Ibrahim

    (University of Abuja)

Abstract

In this study, the panel autoregressive distributed lag (PARDL) approach was used to investigate the link between the sectoral credit of other depository corporations and economic growth in Nigeria. The sectoral GDP used in the study served as a proxy for the dependent variable measuring economic development, and the sectoral credits made by ODCs to 19 different economic sectors, gross fixed capital formation, trade openness, the exchange rate, and the prime lending rate served as independent variables. All datasets, which cover the years 2010Q1 through 2024Q2, were gathered from the FinA application and the Statistical Bulletin of the Central Bank of Nigeria. The data stationarity test was carried out via Im, Pesaran, and Shin (IPS) and Levin, Lin, and Chu (LLC), which revealed that nominal gross domestic product, nominal gross fixed capital formation and foreign direct investment inflow are stationary at level, whereas the prime lending rate, trade openness, ODC sectoral credit, the consumer price index, and the exchange rate are stationary at the first difference, indicating a combination of I(0) and I(1) series. The PARDL analysis shows that the DFE is the preferred model on the basis of the Hausman test. The DFE results revealed that 36.6 percent of the disequilibrium in the Nigerian economy will be corrected in the long run and that NGFCF, TOP, PLR, and CPI influence sectoral economic growth in the short run, whereas SCREDIT, CPI, and FDI have long-run effects on sectoral economic growth in Nigeria. The study suggests several recommendations to increase GDP growth through targeted economic policies, including improving domestic credit accessibility, which is crucial for stimulating investment and consumption; improving trade conditions, which is essential for fostering economic growth; and enhancing financial literacy and education, which is essential for informed financial decisions. Additionally, policymakers should adopt monetary policies that target stable and moderate inflation levels to stimulate investment and consumption. Finally, governments should design incentives, simplify regulatory processes, and foster partnerships between local businesses and foreign investors.

Suggested Citation

  • M. O. Adenomon & Hamman Ibrahim, 2025. "Modelling other depository corporations’ sectoral credit and economic growth nexus in Nigeria: a panel ARDL approach," SN Business & Economics, Springer, vol. 5(4), pages 1-30, April.
  • Handle: RePEc:spr:snbeco:v:5:y:2025:i:4:d:10.1007_s43546-025-00797-9
    DOI: 10.1007/s43546-025-00797-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43546-025-00797-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43546-025-00797-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Beck, Thorsten & Levine, Ross, 2004. "Stock markets, banks, and growth: Panel evidence," Journal of Banking & Finance, Elsevier, vol. 28(3), pages 423-442, March.
    2. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    3. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    4. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    5. Pesaran, M. H. & Shin, Y. & Smith, R. P., 1997. "Pooled Estimation of Long-run Relationships in Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9721, Faculty of Economics, University of Cambridge.
    6. Edward F. Blackburne III & Mark W. Frank, 2007. "Estimation of nonstationary heterogeneous panels," Stata Journal, StataCorp LLC, vol. 7(2), pages 197-208, June.
    7. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, Decembrie.
    8. Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
    9. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samargandi, Nahla & Fidrmuc, Jan & Ghosh, Sugata, 2015. "Is the Relationship Between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle-Income Countries," World Development, Elsevier, vol. 68(C), pages 66-81.
    2. Gangopadhyay, Partha & Jain, Siddharth & Bakry, Walid, 2022. "In search of a rational foundation for the massive IT boom in the Australian banking industry: Can the IT boom really drive relationship banking?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    3. Gautam, Tej K. & Paudel, Krishna P., 2018. "The demand for natural gas in the Northeastern United States," Energy, Elsevier, vol. 158(C), pages 890-898.
    4. Salisu, Afees A. & Ndako, Umar B., 2018. "Modelling stock price–exchange rate nexus in OECD countries: A new perspective," Economic Modelling, Elsevier, vol. 74(C), pages 105-123.
    5. Ravetti, Chiara & Cambini, Carlo, 2021. "Energy Use Beyond GDP: A Dynamic Panel Analysis with Different Development Indicators," Working Papers 10-2021, Copenhagen Business School, Department of Economics.
    6. Gautam, Tej K. & Paudel, Krishna P., 2018. "Estimating sectoral demands for electricity using the pooled mean group method," Applied Energy, Elsevier, vol. 231(C), pages 54-67.
    7. Afees A. Salisu & Kazeem Isah, 2017. "A Capital Flight-Growth Nexus in Sub-Saharan Africa: The Role of Macroeconomic Uncertainty," Working Papers 034, Centre for Econometric and Allied Research, University of Ibadan.
    8. Gamtessa, Samuel & Olani, Adugna Berhanu, 2018. "Energy price, energy efficiency, and capital productivity: Empirical investigations and policy implications," Energy Economics, Elsevier, vol. 72(C), pages 650-666.
    9. Lin, Boqiang & Omoju, Oluwasola E., 2017. "Focusing on the right targets: Economic factors driving non-hydro renewable energy transition," Renewable Energy, Elsevier, vol. 113(C), pages 52-63.
    10. Halkos, George, 2012. "The impact of government expenditure on the environment: An empirical investigation," MPRA Paper 39957, University Library of Munich, Germany.
    11. Joseph David, 2024. "The role of corruption in the oil price–growth relationship: Insights from oil-rich economies," Economic Change and Restructuring, Springer, vol. 57(6), pages 1-32, December.
    12. Zeeshan Arshad & Margarita Robaina & Anabela Botelho, 2020. "Renewable and Non-renewable Energy, Economic Growth and Natural Resources Impact on Environmental Quality: Empirical Evidence from South and Southeast Asian Countries with CS-ARDL Modeling," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 368-383.
    13. Bennouna, Hicham, 2019. "Interest rate pass-through in Morocco: Evidence from bank-level survey data," Economic Modelling, Elsevier, vol. 80(C), pages 142-157.
    14. Siddique, Abu & Selvanathan, E.A. & Selvanathan, Saroja, 2016. "The impact of external debt on growth: Evidence from highly indebted poor countries," Journal of Policy Modeling, Elsevier, vol. 38(5), pages 874-894.
    15. Usman, Muhammad & Makhdum, Muhammad Sohail Amjad, 2021. "What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development," Renewable Energy, Elsevier, vol. 179(C), pages 12-28.
    16. Chakraborty, Saptorshee Kanto & Mazzanti, Massimiliano, 2021. "Renewable electricity and economic growth relationship in the long run: Panel data econometric evidence from the OECD," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 330-341.
    17. Thian-Hee Yiew & Chin-Yu Lee & Lin-Sea Lau, 2021. "Economic growth in selected G20 countries: How do different pollution emissions matter?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11451-11474, August.
    18. Rajesh Sharma & Samaresh Bardhan, 2017. "Finance growth nexus across Indian states: evidences from panel cointegration and causality tests," Economic Change and Restructuring, Springer, vol. 50(1), pages 1-20, February.
    19. Bittencourt, Manoel, 2012. "Financial development and economic growth in Latin America: Is Schumpeter right?," Journal of Policy Modeling, Elsevier, vol. 34(3), pages 341-355.
    20. Mounir Belloumi & Ahmed Aljazea, 2024. "Relationship between Energy and Economic Growth: Evidence from a Panel Nonlinear ARDL Model," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 468-476, March.

    More about this item

    Keywords

    Panel ARDL; Panel unit root; Sectoral GDP; Gross fixed capital formation; Trade openness; Exchange rate; Prime lending rate;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snbeco:v:5:y:2025:i:4:d:10.1007_s43546-025-00797-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.