A predictive model for inflation in Nigeria - Nse S. Udoh; Anietie S. Isaiah (CBN Journal of Applied Statistics, 2018)
The study estimates a dynamic model using quarterly data spanning 1995 to 2016. Four dynamic models: level lagged variables, differenced lagged variables, log-transformed lagged variables and differenced log-transformed lagged variables were considered. The best predictive model was selected based on the Schwarz Information Criterion (SIC) value. From the empirical results, the level form models performed better than the differenced form models. On the basis of model parsimony, the level lagged model was the preferred model among the set of selected models. Predictions obtained from the model indicate that the model is stable as actual interest rate (IR) values, fall well within the computed 95% prediction interval. The study concludes that previous values of IR and money supply (MS) are significant in predicting future ination rates in Nigeria.
Keywords: E52; 330; Autoregressive Distributed Lag Model; Inflation Rate; Predictive Model