Application of Sarima Models in Modelling and Forecasting Nigeria’s Inflation Rates

Abstract

This paper discussed the Application of SARIMA Models in Modeling and Forecasting Nigeria's Inflation Rates. Time series analysis and forecasting is an efficient versatile tool in diverse applications such as in economics and finance, hydrology and environmental management fields just to mention a few. Among the most effective approaches for analyzing time series data, the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA) was employed in this study. In this paper, we used Box-Jenkins methodology to build ARIMA model for Nigeria's monthly inflation rates for the period November 2003 to October 2013 with a total of 120 data points. In this research, ARIMA (1, 1, 1) (0, 0, 1)12 model was developed, and obtained as 1t y + = 0.3587y t +0.6413y t-1 -0.8840e t-11 -0.7308912e t-12 +0.8268e t . This model is used to forecast Nigeria's monthly inflation for the upcoming year 2014. The forecasted results will help policy makers gain insight into more appropriate economic and monetary policy in other to combat the predicted rise in inflation rates beginning the first quarter of 2014.

Forecasting Inflation Rate in Kenya Using SARIMA Model

“…In this study, the model with the minimum value of AIC is judged as the best model. It is given by AIC=-2 In(L) + 2k (6) where k= p+q+1 and L is the maximized likelihood value.…”

A Predictive Model for Inflation in Nigeria

“…Perhaps in an attempt to obtain a more robust view of the dynamics of inflation in Nigeria, some authors have used the univariate time series and the error correction and cointegration approaches, employing money growth, income and exchange rate movements as the focal variables. Examples of the use of the univariate time series models include works by Doguwa and Alade (2013), Otu et al (2014) and Etuk (2017). The error correction and cointegration modelling approach include studies by Folorunso and Abiola (2000); Odusanya and Atanda (2010); Maku and Adelewokan (2013).…”

Short-Term Forecasting of Nigeria Inflation Rates Using Seasonal ARIMA Model

“…Also, [8] modeled the same Inflation rates within the same period using SARIMA (0, 1, 1) x (0, 1, 1), but did not show the forecast adequacy. Further, [19] applied seasonal multiplicative ARIMA model, SARIMA (1, 1, 1) x (0, 0, 1) 12 to model Nigeria Inflation rates from November, 2003 to October 2013. Others made use of Consumer Price Index to model Inflation rates.…”

“…[8] in their study in forecasting Nigeria inflation rates used multiplicative seasonal autoregressive integrated moving average (ARIMA) model, (0, 1, 1) x (0, l, l) 12 and fitted the series and made forecast obtained on the basis of the model. [19] applied SARIMA models in modeling and forecasting Nigeria's inflation rates using Box -Jenkins methodology to build ARIMA model (1, 1, 1) x (0, 0, l) 12 and used this model in forecasting Nigeria's rates. Doguwa and Alade [6] proposed four short term headline inflation forecasting models in Nigeria using the SARIMA and SARIMAX processes and compared their performances using the pseudo-out-of sample forcasting procedure over July, 2011 to September, 2013.…”