STATISTICAL ANALYIS OF INFLATION RATE IN ETHIOPIA: THRESHOLD AUTO REGRESSIVE APPROACH

Abstract

Inflation is the industrious and non stop ascent in the overall prices of any given commodity in an economy. It is among the most macroeconomic variable described nonlinear behavior. The aim of this study was also to model and forecast inflation in Ethiopia using nonlinear models and to establish the existence of nonlinear patterns in the consumer price index. The study utilized the secondary data collected from Monthly data of consumer price index for inflation rate from January 1994 to December 2020 which was obtained from central statistical Agency. The average monthly inflation rate was obtained 53.2800 with standard deviation of 45.980 during the study period. The result showed that monthly rate of inflation was characterized by a none constant mean and an unstable variance implying a non stationary the series and achieved stationarity by differecing orders. Nonlinearity tests result based on Tsay tests showed non linearity of consumer price index and the SETAR (2,4,4) had the minimum value for both Akakie Information Criteria and Bayesian Information criterion among the camdidate models considered for the study. After modeling the inflation series was made, the comparison of the forecast performance between the nonlinear time series models and linear ARMA models based on forecast measure of mean absolute error (MAE), means absolute percentage error (MAPE), and mean absolute scale error. This forcasting performance comparison result showed that the nonlinear TAR family models suggest that the nonlinear SETAR model outperform the linear ARMA models. The in-sample forecast using the best-fit asymmetric model, that is SETAR (2,4,4) model indicates that the consumer price index the series exhibits an upward trand year 2001 to 2010 and then almost similar livel 2011 to 2018 and then decrease at the end of study period. Based on this result it can be recommended that, by using the Threshold Auto regressive Models policy makers would be able to properly capture the price volatility persistence and hence forecasts and estimates would be more accurate.