 Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in NigeriaA chapter in Bayesian Networks - Advances and Novel Applications from IntechOpen 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. Keywords: Bayesian graphical networks; SVAR; MCMC; M-H; Granger-causal inference; Nigeria (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:168069 Access Statistics for this chapter More chapters in Chapters from IntechOpen |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.