A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria

Abstract

This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook real time data of the U.S. Federal Reserve's in the analysis of monetary policy reaction functions in forecasting performance using ANN. Following the work on the adoption of this technique, we tried to develop a framework based on machine learning for policy rate forecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policy decisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and the univariate process. It also revealed the non-linearity in the behavior of the monetary policy rate in Nigeria during the study period. While the work does not mean to advocate that machine will replace human-being in policy rate determination in the monetary policy-making process; we believe that the development and implementation of this system would support building effective prediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence and transparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in setting policy through a well-structured comparison of results.