A Neural Network Scheme for Validating Monetary Policy Rates in Nigeria

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 bothoptimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook realtime 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 rateforecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policydecisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and theunivariate 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 monetarypolicy-making process; we believe that the development and implementation of this system would support building effectiveprediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence andtransparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in settingpolicy through a well-structured comparison of results.