Zhang, X., Tao, X. and Jin, Y. (2023) Detecting Influential Subjects in Intensive Longitudinal Data using Mixed‐Effects Regression. BMC Medical Research Methodology, 23, Article No. 32.

has been cited by the following article:

• TITLE: Random Coefficient Modelling of the Global Effect of Exchange and Monetary Policy Rates on Inflation (2)

  AUTHORS: Chioma Okoronkwo, Geoffery Uzodinma Ugwuanyim, Hycinth Chukwudi Iwu, Harrison Obiora Amuji

  KEYWORDS: Monetary Policy Rate, Inflation, Rate, Random Slope, Frequentist Linear Mixed-Model, Bayesian Linear Mixed-Model

  JOURNAL NAME: Open Journal of Statistics, Vol.15 No.6, December 24, 2025

  ABSTRACT: This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It extends the first part of this research which analyzed the relationship between exchange rate and MPR on inflation using the Generalized Additive Mixed Model under both the Frequentist and Bayesian frameworks. The Frequentist LMM revealed a positive and significant relationship between MPR and inflation, indicating that higher MPR is associated with increased inflation, though the effect varied minimally across continents. The Bayesian LMM produced a positive posterior mean for MPR, but its 95% credible interval included zero, reflecting uncertainty about the magnitude of the effect. Posterior predictive checks and convergence diagnostics confirmed model adequacy. The impact of monetary policy was stronger in Asia and North America but negligible in Africa, Europe and South America. Overall, the Bayesian LMM demonstrated superior flexibility, predictive accuracy and richer uncertainty representation.