The Dynamic Impact of Oil Price Shocks on Sectoral Inflation in Morocco: A Bayesian Factor-Augmented VAR

Abstract The present study employs a Bayesian Factor-Augmented Vector Autoregression (B-FAVAR) model with monthly data from January 2007 to December 2025 to examine the dynamic transmission of global oil price shocks to sectoral inflation in Morocco. The framework extracts latent factors from 36 macroeconomic and financial variables, enabling a comprehensive analysis of oil price transmission across thirteen disaggregated CPI components. Using recursive Cholesky identification with oil prices ordered first, we find substantial heterogeneity across sectors. The transport sector exhibits the strongest and most immediate response, while housing responds with a short lag consistent with regulated pricing. Food inflation shows a gradual but persistent response through indirect channels, whereas communication, education, and recreation exhibit negligible effects. Oil price shocks explain approximately 25 percent of transport inflation variability at the 12-month horizon. The money market rate responds positively, consistent with monetary policy tightening, while effective exchange rates show insignificant responses, reflecting Morocco’s managed exchange rate regime. These findings underscore the mitigating role of the compensation fund and the exchange rate regime, though ongoing subsidy reforms may alter this transmission mechanism. Policy recommendations include careful calibration of exchange rate flexibility, implementation of targeted transfers to protect vulnerable households, and adoption of a forward-looking monetary policy approach given the delayed transmission to credit aggregates. This study demonstrates the value of the B-FAVAR methodology for analysing oil price shocks and provides evidence-based insights for enhancing macroeconomic resilience. Keywords: Bayesian FAVAR, oil price shocks, sectoral inflation, monetary policy, Morocco, factor models