Multivariate Time Series Analysis Of Inflation The Case Of Ethiopia

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

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