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The Impact of Money Supply on Nigeria Economy: A Comparison of Mixed Data Sampling (MIDAS) and ARDL Approach

Author

Listed:
  • Adeniji Sesan Oluseyi

    (University of Abuja)

  • Timilehin John Olasehinde

    (Ekiti State University)

  • Gamaliel O. Eweke

    (Federal University Otueke)

Abstract

The study investigates the long and short run relationships between broad money supply and real aggregate output (GDP) in Nigeria from 1981 to 2015. This study set to investigate the perplexity whether or not money supply as the major monetary policy measures actually impact on the Nigerian economy. This work made use of data of different frequencies (yearly and quarterly) in order to reveal some hidden facts that data of the same frequency might fail to show. An unrestricted version of Mixed Data Sampling (U-MIDAS) technique and Autoregressive Distributed Lag (ARDL) technique were employed. The ADF unit root test revealed that the yearly real GDP and quarterly broad money supply contained a unit root and this permit the testing of cointegration among the variables. The U-MIDAS results affirm the existence of a long and shortrun relationship between yearly real GDP and quarterly broad money supply at different season while the ARDL result affirm that money supply impacted significantly on real GDP in the long run only. The study concluded that the disequilibrium correction terms from the two analytical approaches showed the evidence that there is a tendency for growth targeting in Nigeria which is one of the major objectives of Nigeria economy though at a slower rate. It was therefore recommended that monetary authority should maintain the level of inflation targeting in the economy and the volume of money to be supply should be monitored as too much money supply in the economy will lead to skyrocketing inflation and also the periodic money multiplier should be made efficient by supplying the money into the circulation regularly so as to co-trend with the real GDP growth by making cash available for business transactions and other economic activities, this will by means improve the real GDP of Nigeria economy.

Suggested Citation

  • Adeniji Sesan Oluseyi & Timilehin John Olasehinde & Gamaliel O. Eweke, 2017. "The Impact of Money Supply on Nigeria Economy: A Comparison of Mixed Data Sampling (MIDAS) and ARDL Approach," EuroEconomica, Danubius University of Galati, issue 2(36), pages 123-134, November.
    • Handle: RePEc:dug:journl:y:2017:i:2:p:123-134
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    References listed on IDEAS

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    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. AfDB AfDB, . "African Statistical Journal Vol. 5," African Statistical Journal, African Development Bank, number 53 edited by Koua Louis Kouakou, March.
    3. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.
    4. Fazal Husain & Kalbe Abbas, 2000. "Money, Income, Prices, and Causality in Pakistan. A Trivariate Analysis," PIDE-Working Papers 2000:178, Pakistan Institute of Development Economics.
    5. Franses, Ph.H.B.F., 2016. "Yet another look at MIDAS regression," Econometric Institute Research Papers EI2016-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    7. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    8. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    9. Díaz-Emparanza Herrero, Ignacio & Moral Zuazo, María Paz, 2013. "Seasonal Stability Tests in gretl. An Application to International Tourism Data," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
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