Vector Autoregressive and Autoregressive Integrated Moving Average Modelling of inflation Rate in Nigeria: time series estimates dynamic causal effects and correlation over time.

Abstract

Inflation is an important indicator of economic activity often used by decision makers to plan economic policies and remains a subject of utmost concern and interest to policy makers. This work models inflation over the period 1990-2018, using autoregressive integrated moving average (ARIMA) and Vector Auto regression (VAR) modeling techniques. ARIMA model is used to fit Nigerian historical consumer price index (CPI) time series expressed in terms of past values of itself plus current and lagged values of error term. Data for the last six months were used to evaluate the performance of the prediction. VAR model is used to investigate the effect of money supply, Nigeria oil prices and exchange rate on inflation rate over the same period. Unit root test (Augmented Dickey- Fuller test) was used to check the integration order of the variables. A cointegration analysis with the four variables was then employed. Both the trace test and max Eigen value statistics revealed that individual variables are cointegrated with inflation at 5% significant level. A Vector Error Correction Model (VECM) was then estimated. It was observed that there is no long run causality running from the independent variables to inflation. In addition, money supply and exchange rate has no short run causality to inflation. The result ARIMA model shows that ARIMA (1,1,0) was the best model out of the fitted ARIMA models.