Forecasting Inflation Rate in Kenya Using SARIMA Model

Abstract

It is the desire of the policy makers in a country is to have access to reliable forecast of inflation rate. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast Kenya's inflation rate using quarterly data for the period 1981 to 2013 obtained from KNBS. SARIMA (0,1,0) (0,0,1) 4 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. Diagnostic checks using Jarque-Bera Normality Test indicated that they were normally distributed. ACF and PACF plots for the residuals and squared residuals revealed that they followed a white noise process and were homoskedastic respectively. The predictive ability tests RMSE=0.2871, MAPE=3.9456 and MAE= 0.2369 showed that the model was appropriate for forecasting the inflation rate in Kenya.

Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022

“…For seasonal and non-seasonal data, the SARIMA (seasonal model autoregressive integrated moving average) is used. The SARIMA model is an extension of the simple ARIMA models, being used for inflation forecasting [ 19 , 20 , 21 ], for exchange rate forecasting [ 22 , 23 ], for tourist arrivals and income forecasting [ 24 , 25 ], as well as for unemployment forecasting. The literature includes a lot of studies on forecasting using ARIMA models, respectively the Box–Jenkins methodology, which is widely used by many researchers to highlight future unemployment rates [ 26 ].…”

On the Analysis and Modelling of the Harmonized Consumer Price Indices of West African Economic and Monetary Union Member States

“…where 1 : , 5 ; , 6 et 8 < are such that the stationarity and the invertibility of the entire model are guaranteed. Equations (4) and (6) are respectively the non-seasonal autoregressive and moving averages operators and equations (5) and (7) are their seasonal equivalents.…”

“…Many research works were conducted on price indices and inflation in general and HCPI in particular. ARIMA models [2] were used to study the inflation rate and price indices [3][4][5][6]. Gigunku and al.…”

“…Gigunku and al. proposed a (0,1,0)(0,0,1) model to explain and forecast inflation in Kenya [4] whereas Habimana and al. suggested an (4,1,6) model for prices index of Rwanda [6].…”

Forecasting Foreign Trade of Bosnia and Herzegovına for Wood and Articles of Wood, Wood Charcoal by Seasonal ARIMA Model

“…Here, w t is the differenced series, p is order of the autoregressive part and q is order of moving average part. Also, we said this an ARIMA (p,d,q), where, d is a ARIMA model was applied many studies [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. In this study, it is aimed that the analysis export and import values of Bosnia and Herzegovina for wood and articles of wood with SARIMA model.…”