Analysis of an Optimal Short-Term Inflation Rate Forecasting Model in Kenya Case of SARIMA Modelling

Purpose: Inflation has soared to multi decade highs prompting rapid monetary policy tightening and squeezing household budgets. Combining several techniques of forecasting is an instinctual way to improve prediction performance as the limitations of one method are compensated by the strength of the other model. It is real that uncertainty over future inflation forecasting has caused detrimental and negative impact not only globally but also in the Kenyan economy. The general objective of the study was to develop an optimal model of forecasting short term inflation rate in Kenya. The specific objectives were to establish models of forecasting short term inflation rate using SARIMA, GARCH and hybrid SARIMA-GARCH family Models. Select an optimal model amongst the three models. Predict 12 months ahead inflation rate using the optimal model. Scope of the study covered was 2005m1 to 2024m4.This study was anchored on monetary theory of inflation, Keynesian theory of inflation as well as rationale expectation theory of inflation. Data was sourced from KNBS, CPI monthly from January 2005 to April 2024. Methodology: The study was guided by positive research design philosophy. Explanatory research design was used in this study. Target population was 230 monthly observations in Kenya from January 2005 to April 2024.PACF and ACF were used to check autocorrelation in the data. Diagnostic checks The Ljung-Box test (LL) and Q2 indicated non-significant autocorrelation p- value were greater than 05% indicating that the models residuals were white noise. Findings: The DOF/GED parameter (4.144466*, 1.101958, 6.977499) represented the degrees of freedom for the t-distribution and the coefficients where significant at 0.05% meaning the model's assumptions for normal distributions were met. The following models SARIMA (1,0,1)(2,1,0)12 (Aic133.99), GARCH (1, 1) model ( aic 1.63). Hybrid, SARIMA((1,0,1),(2,1,0)) gjrGARCH(1,1) model(aic 1.34) were identified and estimated. Implication of these results were that in terms of the aic hybrid SARIMA gjrGARCH model had the lowest (aic 1.33) and was chosen as the optimal model for forecasting Kenya’s inflation rate amongst the three models. The coefficients of AR Normal 0.048140, T-Student −0.031854, GED −0.02396 and MA Normal 0.055167, T-Student 0.059645, GED 0.051390 terms were significant at 5%. The results implied that the model predicted a decrease in the Kenya’s inflation rate for the next 12 months. In terms of forecast accuracy the lowest (RMSE) and (MAE) criterion the optimal model for forecasting short term inflation rate in Kenya was hybrid SARIMA((1,0,1),(2,1,0)) gjrGARCH(1,1) with lowest coefficient (MAE) of 11.45 and (RMSE) of 12.84. The study recommended that inflation rate would be hovering below an average rate of 10 within the next 12 months up to April 2025 and policy makers should use this prediction for planning in order to maintain Kenya’s macroeconomic stability. Unique Contribution to Theory, Practice and Policy: Policy makers were also advised to use the hybrid model to forecast short term inflation rate 12 months ahead in future years. The benefits of the study was added knowledge of hybridizing (methodological) to researchers and theoretical to policy makers.