A Univariate Time Series Analysis of Nigeria’s Monthly Inflation Rate
DOI:
https://doi.org/10.46881/ajsn.v1i1.22Keywords:
Inflation rate, Seasonal Autoregressive Integrated Moving Average, Forecasting, Parameters and Model Building.Abstract
Inflation refers to general increase in prices and fall in the purchasing power or value of money. le Inflation has always been undesirable for any nation’s economy in the world. This paper discusses and analyses the fluctuations and volatility in Nigeria’s inflation rates. The methodology employed in the analysis and modelling of the inflation rates was Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The identification stage of the model building suggests four models for Nigeria’s inflation forecast, but was validated in the estimation stage using Akaike Information Criteria (AIC), Shwart Bayesian Criteria (SBC), Hanna Quinn Criteria (HQC) parameters. The model was diagnosed and the results showed that the model was adequate and parsimonious. Hence, was fitted to obtain forecast values on monthly bases with minimum root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) parameters respectively. In conclusion, the model was a good fit with minimum parameters and the forecast generated revealed high fluctuation and volatility in Nigeria’s inflation rates. The forecast results will help policy makers to gain an insight into more appropriate economic and monetary policy in order to combat the predicted rise and fall of Nigerian inflation rates.
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