Multivariate Time Series Analysis of Inflation: The Case of Ethiopia

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title


Addis Abeba university


Inflation refers to a situation in which the economy’s overall price level is rising. The inflation rate is the percentage change in the price level from the previous period. The measures of inflation are various price indices, such as a consumer price index (CPI), producer price index (PPI), or GDP deflator. However, inflation is usually defined as a change in the CPI over a year. The aim of this study is to fit a time series model for CPI and its components which can be used to forecast the rate of inflation in Ethiopia. The data used are monthly observations from January 2000 to December 2010 of the Consumer Price Index (CPI), Food Price Index (FPI) and Non-food Price Index (NFPI). The vector autoregressive (VAR) model is employed for modeling. The cointegration relations among the price indices were identified by applying Johansen’s cointegration tests, while potential causal relations were examined by employing Granger’s causality tests. Moreover, the short run interactions among the variables were determined through the application of impulse response analysis and variance decomposition. The results of the research imply the existence of short term adjustments and long-term dynamics in the CPI, FPI and NFPI. Unit root test reveals that all the series are non stationary at level and stationary at first difference. The result of Johansen test indicates the existence of one cointegration relation between the variables. The final result shows that a Vector Error Correction (VEC) model of lag two with one cointegration equations best fits the data. The forecasting accuracy of this model was checked using RMSE, MAE, MAPE and Theil-U statistics. Finally, using the fitted model out-of-sample forecasts were produced for Ethiopian inflation rate. Keywords: Inflation, Vector autoregressive, co-integration, Vector Error correction model and forecasting



Inflation, Vector Autoregressive, Co-Integration, Vector Error Correction Model and Forecasting