# Econometric assessment of the monetary policy shocks in Morocco: Evidence from a Bayesian Factor-Augmented VAR

## TL;DR

This paper employs a Bayesian Factor-Augmented VAR model to analyze the effects of monetary policy shocks on Morocco's economy, addressing dimensionality issues and improving shock identification using extensive macroeconomic data.

## Contribution

It introduces a FAVAR approach with Bayesian methods to better identify and analyze monetary policy shocks in Morocco, overcoming limitations of traditional models.

## Key findings

- Impulse responses for 117 macroeconomic indicators
- Enhanced understanding of monetary policy impacts in Morocco
- Addresses empirical anomalies in traditional VAR models

## Abstract

The analysis of the effects of monetary policy shocks using the common econometric models (such as VAR or SVAR) poses several empirical anomalies. However, it is known that in these econometric models the use of a large amount of information is accompanied by dimensionality problems. In this context, the approach in terms of FAVAR (Factor Augmented VAR) models tries to solve this problem. Moreover, the information contained in the factors is important for the correct identification of monetary policy shocks and it helps to correct the empirical anomalies usually encountered in empirical work. Following Bernanke, Boivin and Eliasz (2005) procedure, we will use the FAVAR model to analyze the impact of monetary policy shocks on the Moroccan economy. The model used allows us to obtain impulse response functions for all indicators in the macroeconomic dataset used (117 quarterly frequency series from 1985: Q1 to 2018: Q4) to have a more realistic and complete representation of the impact of monetary policy shocks in Morocco.