IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/168069.html
   My bibliography  Save this book chapter

Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in Nigeria

In: Bayesian Networks - Advances and Novel Applications

Author

Listed:
  • David Oluseun Olayungbo

Abstract

This study applies Bayesian graphical networks (BGN) using Bayesian graphical vector autoregressive (BGVAR) model with efficient Markov chain Monte Carlo (MCMC) Metropolis-Hastings (M-H) sampling algorithm in a dynamic interaction among monetary policies and macroeconomic performances in Nigeria for the period of 1986Q1-2017Q4. The motivation stems from the instability in the movement of exchange rate, inflation rate and interest rate in Nigeria over the past years as a result of the structure of the economy. In this way, the monetary authority periodically applies the various policy instruments to stabilize the economy using reserve and money supply as at when due. This study adapts VAR and SVAR structure to examine the dynamic interaction among variables of interest, using BN, to provide a better understanding of the monetary policy dynamics and fit the changing structure of the Nigeria's economy as regards the dynamics in her economic structure. Our results show that inflation is the strong predictor of interest rate in Nigeria. A monetary policy of broad inflation targeting is recommended for the country.

Suggested Citation

  • David Oluseun Olayungbo, 2019. "Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in Nigeria," Chapters, in: Douglas McNair (ed.), Bayesian Networks - Advances and Novel Applications, IntechOpen.
    • Handle: RePEc:ito:pchaps:168069
    DOI: 10.5772/intechopen.87994
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/68138
    Download Restriction: no
    File URL: https://libkey.io/10.5772/intechopen.87994?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
    ---><---

    More about this item

    Keywords

    Bayesian graphical networks; SVAR; MCMC; M-H; Granger-causal inference; Nigeria;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    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:ito:pchaps:168069. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.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.