Econometric and AI-Based Modelling of Nigeria's Interest Rates Based on Fisher Equation

In this study, linear and non-linear models are employed to model the Central Bank Rate (CBR), Deposit Interest Rate (DIR), Lending Rate (LER), and Treasury Bill Rate (TBR). The choice of variables is guided by the Fisher Equation (FE). The linear models range from Autoregressive Integrated Moving Average (ARIMA), Simple Linear Regression (SLR), Autoregressive Distributed Lag (ARDL) model, Vector Autoregressive (VAR) model, Bayesian VAR (BVAR) to Vector Error Correction Model (VECM). The nonlinear models include Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that VECM outperforms other models in the testing sub-sample, while ANFIS produces the highest forecast accuracy in the training sub-sample. Ensembling the VECM and ANFIS produces the best forecast accuracy for both the sub-samples. The results of this study can serve as a reference for modeling interest rates and provide the benchmark for investment decisions and macroeconomic policy designs.