IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/88773.html
   My bibliography  Save this paper

Investigating Predictors of Inflation in Nigeria: BMA and WALS Techniques

Author

Listed:
  • Tumala, Mohammed M
  • Olubusoye, Olusanya E
  • Yaaba, Baba N
  • Yaya, OlaOluwa S
  • Akanbi, Olawale B

Abstract

The recent economic conundrum arising from the fall in the international oil price has threatened the maintenance of price stability, a key function of the central bank, therefore the need to investigate predictors of inflationary measures arises. The model averaging method considers uncertainty as part of the model selection, and include information from all candidate models. We analysed a wide spectrum of inflation predictors and all the possible models for Nigeria CPI inflation using the Bayesian Model Averaging and Weighted Average Least Squares. The study uses fifty-nine (59) predictor variables cutting across all sectors of the Nigerian economy and three (3) measures of inflation, namely; all items consumer price index, core consumer price index and food consumer price index. The results from both model averaging techniques showed that maximum lending rate, world food price index and Bureau de change exchange rate are the significant drivers of inflationary measures among focus variables, while foreign assets, credit to private sectors, net credit to government and real effective exchange rate are the drivers of inflationary measures, for the auxiliary variables, strongly supporting the monetarist and open economy views on inflation. The structuralist view is reported to be relatively weaker because government expenditure is only significant at 10.0 per cent..

Suggested Citation

  • Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Investigating Predictors of Inflation in Nigeria: BMA and WALS Techniques," MPRA Paper 88773, University Library of Munich, Germany, revised Feb 2018.
    • Handle: RePEc:pra:mprapa:88773
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/88773/1/MPRA_paper_88773.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    2. Magnus, J.R. & Powell, O.R. & Prüfer, P., 2008. "A Comparison of Two Averaging Techniques with an Application to Growth Empirics," Other publications TiSEM 0392dffa-51e0-4bc9-9644-f, Tilburg University, School of Economics and Management.
    3. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    4. Olusanya E. Olubusoye & Rasheed Oyaromade, 2008. "Modelling the Inflation Process in Nigeria," Working Papers 182, African Economic Research Consortium, Research Department.
    5. M S Mohanty & Marc Klau, 2001. "What determines inflation in emerging market economies?," BIS Papers chapters, in: Bank for International Settlements (ed.), Modelling aspects of the inflation process and the monetary transmission mechanism in emerging market countries, volume 8, pages 1-38, Bank for International Settlements.
    6. Oa Akinboade & Ew Niedermeier & Fk Siebrits, 2002. "The Dynamics of Inflation in South Africa: Implications for Policy*(1)," South African Journal of Economics, Economic Society of South Africa, vol. 70(3), pages 213-223, March.
    7. James E. Glassman & David J. Stockton, 1983. "An evaluation of alternative price forecasting models: theoretical considerations," Working Paper Series / Economic Activity Section 32, Board of Governors of the Federal Reserve System (U.S.).
    8. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
    9. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 26(Q I), pages 32-44.
    10. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
    11. Emanuele Bacchiocchi, 2003. "Modelling Italian Inflation, 1970 - 1998," Economics and Quantitative Methods qf0318, Department of Economics, University of Insubria.
    12. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2010. "A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 720-754.
    13. Todd E. Clark & Edward S. Knotek & Saeed Zaman, 2015. "Measuring Inflation Forecast Uncertainty," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2015(03), pages 1-6, March.
    14. Magnus, Jan R. & Powell, Owen & Prüfer, Patricia, 2010. "A comparison of two model averaging techniques with an application to growth empirics," Journal of Econometrics, Elsevier, vol. 154(2), pages 139-153, February.
    15. Jan R. Magnus & J. Durbin, 1999. "Estimation of Regression Coefficients of Interest When Other Regression Coefficients Are of No Interest," Econometrica, Econometric Society, vol. 67(3), pages 639-644, May.
    16. Edward S. Knotek & Saeed Zaman, 2013. "When Might the Federal Funds Rate Lift Off? Computing the Probabilities of Crossing Unemployment and Inflation Thresholds," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
    17. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    18. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
    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. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2018. "Weighted-average least squares estimation of generalized linear models," Journal of Econometrics, Elsevier, vol. 204(1), pages 1-17.
    2. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.
    3. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    4. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    5. Karen Poghosyan & Jan R. Magnus, 2012. "WALS Estimation and Forecasting in Factor-based Dynamic Models with an Application to Armenia," International Econometric Review (IER), Econometric Research Association, vol. 4(1), pages 40-58, April.
    6. Aiyar, Shekhar & Duval, Romain & Puy, Damien & Wu, Yiqun & Zhang, Longmei, 2018. "Growth slowdowns and the middle-income trap," Japan and the World Economy, Elsevier, vol. 48(C), pages 22-37.
    7. Dardanoni, Valentino & Modica, Salvatore & Peracchi, Franco, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Journal of Econometrics, Elsevier, vol. 162(2), pages 362-368, June.
    8. Srdelić, Leonarda & Dávila-Fernández, Marwil J., 2024. "International trade and economic growth in Croatia," Structural Change and Economic Dynamics, Elsevier, vol. 68(C), pages 240-258.
    9. Srdelic, Leonarda & Davila-Fernandez, Marwil J., 2022. "Demographic transition and economic growth in 6-EU member states," MPRA Paper 112188, University Library of Munich, Germany.
    10. Berger, Michael & Pock, Markus & Reiss, Miriam & Röhrling, Gerald & Czypionka, Thomas, 2023. "Exploring the effectiveness of demand-side retail pharmaceutical expenditure reforms: cross-country evidence from weighted-average least squares estimation," LSE Research Online Documents on Economics 116928, London School of Economics and Political Science, LSE Library.
    11. Michael Berger & Markus Pock & Miriam Reiss & Gerald Röhrling & Thomas Czypionka, 2023. "Exploring the effectiveness of demand-side retail pharmaceutical expenditure reforms," International Journal of Health Economics and Management, Springer, vol. 23(1), pages 149-172, March.
    12. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2013. "Bayesian Model Averaging for Generalized Linear Models with Missing Covariates," EIEF Working Papers Series 1311, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
    13. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
    14. Hagen, Tobias, 2013. "Impact of national financial regulation on macroeconomic and fiscal performance after the 2007 financial stock: Econometric analyses based on cross-country data," Working Paper Series 02, Frankfurt University of Applied Sciences, Faculty of Business and Law.
    15. Becker William & Paruolo Paolo & Saltelli Andrea, 2021. "Variable Selection in Regression Models Using Global Sensitivity Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 13(2), pages 187-233, July.
    16. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    17. Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
    18. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2012. "A generalized missing-indicator approach to regression with imputed covariates," Stata Journal, StataCorp LP, vol. 12(4), pages 575-604, December.
    19. Poudineh, Rahmatallah & Jamasb, Tooraj, 2016. "Determinants of investment under incentive regulation: The case of the Norwegian electricity distribution networks," Energy Economics, Elsevier, vol. 53(C), pages 193-202.
    20. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.

    More about this item

    Keywords

    Bayesian estimation; BMA; Frequentist approach; Inflation rate;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:88773. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.