Autoregressive Distributed Lag Model, Inﬂation Rate, Predictive Model
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 inﬂation rates in Nigeria.
CBN Journal of Applied Statistics
Udoh, Nse S. and Isaiah, Anietie S.
"A Predictive Model for Inﬂation in Nigeria,"
CBN Journal of Applied Statistics (JAS): Vol. 9
, Article 5.
Available at: https://dc.cbn.gov.ng/jas/vol9/iss2/5