Purpose: The study aims to study the endogeneity of the money supply in Rwanda. The objective is to identify the empirical evidence regarding the extent and characteristics of this endogeneity specifically concerning variables such as monetary base, credit, demand deposit, and industrial production index.
Design/ Methodology/ Approach: The study utilizes Rwandan data from January 2012 to December 2022, the study used data provided by the National Bank of Rwanda and the National Institute of Statistics of Rwanda. The study employs descriptive analysis which involves examining descriptive statistics for each variable to understand the data's characteristics and assess the dispersion of the data points from their mean, time series analysis, cointegration testing, Vector Autoregressive (VAR) modelling, and Vector error correction model (VECM), were also be used in the research.
Findings: This research reveals that in Rwanda, the monetary base and overall monetary aggregates are primarily influenced by the extent of lending by commercial banks and demand deposits. It identifies a short-term causal relationship wherein the monetary base affects bank loans and the money supply. However, in the short term, the quantity of deposits, money supply, and the monetary base appear to have no direct impact on the volume of loans extended by commercial banks. The models validate that monetary indicators are chiefly influenced by the industrial production index, bank deposits, and the volume of loans issued by commercial banks
Research Limitation/Implications: Establishing clear causality between money supply and economic variables such as output and inflation is challenging due to the potential for bidirectional causality. The money supply can influence these variables, and vice versa, making it difficult to identify the direction and magnitude of causal relationships.
Practical Implications: The study's emphasis on the pivotal role played by commercial banks and their lending activities in the creation of money in Rwanda provides valuable insights into the country's banking sector dynamics.
Social Implications: This knowledge can be used to design policies that promote financial inclusion, ensuring banks' activities contribute to wider economic goals.
Originality/ Value: The use of a range of analytical techniques, including descriptive analysis, time series analysis, cointegration testing, Vector Autoregressive (VAR) modelling, and Vector error correction model (VECM), adds rigour to the analysis and enhances the robustness of the findings.