Gemechu Bekana Fufa



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 time. The aim of this study is to identify the key determinants of inflation by using regression models in Illu Abba Bor zone which can be used to forecast the rate of inflation in study area. A random sample of 408 was selected using multistage random sampling from the study area. Multiple regression Models, Logistic regression models, Vector Autoregressive (VAR) Models, Testing Stationary: Unit root test, Estimating Order of the VAR, Co integration Analysis (testing of co integration) and Vector Error Correction (VEC) Models and coefficients of determination methods of data analysis were used in this study. Comparisons were made between food price index and non-food price index using the Z- test and regression analysis. The findings of the study suggest that the percentage of food price index in higher than that of non-food price index. The determinants of inflation differ between sectors (food and non-food) and the time horizons under consideration. The most important forces behind inflation were money supply, access of agricultural products, Tax, Exchange rates, Infrastructure, Access of raw material for production, Import and Producers price index. Similarly, 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. Finally, using the fitted model out-of-sample forecasts were produced for I/A/B inflation rate. To contain inflation, therefore, the policy interventions aimed at tackling the current determinants of inflation need to take into account the priorities of the government as the effect of policy instruments and means of solutions.

The examination uncovered the job of social duty and condition administration as a commitment in securing the enthusiasm of the states; financial specialists; people groups and contrasting it and the Prohibition of Execution Requirement (PPR) as for the most part presented in universal venture understanding, especially in speculation section under FTA.


Inflation, Vector autoregressive, Vector Error correction model, Multiple Regression, Lo-gistic regression, forecasting


Abdullah, M. and R. Kalim, 2012. Empirical Analysisof Food Price Inflation in Pakistan” World Applied Sciences Journal, 16(7): 933-939. Agresti, A. (2007), an Introduction to Categorical Data Analysis. John Wiley And Sons, Inc, New York Central Statistical Agency (CSA, 2005).
Report of the 2005. Federal Democratic Repub-lic of Ethiopia, Addis Ababa. Central Statistical Authority (2001).
Statistical Abstract Fielding, D., 2008, ‘Inflation Volatility and Economic Development: Evidence from Nigeria’, University of Otago Economics Discussion Papers No. 0807, 1 – 22. Geda A. and Tafere K. (2008): “The Galloping Inflation in Ethiopia: A Cautionary Tale for Aspiring ‘Developmental States’ in Africa”, Addis Ababa University working paper series Hair, J.F., Black, W., Babin, B.J., Anderson, R.E. (2009). Multivariate Data Analysis, 7th edition, Prentice Hall. Hosmer D.W. & Lemeshow S. (2004). Applied Logistic Regres-sion, 2nd ed. Hamilton, J.D. (1994): Time Series Analysis. Princeton University Press, Princeton. Healy, L., (2006) “Logistic Regression: An Overview”. Eastern Michighan College of Technology. Hilbe, J. M.(2009), Logistic Regression Models, Chapman & Hall, London Hosmer, D.W. and Lemeshow, S. (1989). Applied Logistic Regression, John Wliey & Son, Inc. Hosmer, W.D. and S. Lemeshow (2000), Applied Logistic Regression. 2nd Ed., John Wiley and Sons, New York.

Hosmer, W.D. and S. Lemeshow (2000), Applied Logistic Regression. 2nd Ed., John Wiley and Sons, New York. Jema, H., and Fekadu, G. (2012), “Determinates of the Recent Soaring Food Inflation in Ethiopia”, Universal Journal of Education and General Studies, Johansen, S. (1988):“Statistical Analysis of Cointegration Vectors”. Journal of Economic Dynamics and Control, 12, 231-254. John ,M., Worku ,S. and Paulos Z. (2009): “Impact of Soaring Food prices in Ethiopia”. International Food Policy Research Institute (IFPRI), Discussion paper 00846. Keith, T. (2006). Multiple regression and beyond. PEARSON Allyn & Bacon. Khan, M.S., & Senhadji, A.S., 2001, ‘Threshold Effects in the Relationship between In-flation and Economic Growth’ IMF Staff Papers 48(1), 1-21. Lutkepohl, H. (1991): Introduction to Multiple Time Series Analysis .Springer-Verlag, Berlin. Mantel, N. (1970), “Why Stepdown Procedures in in Variable Selection,” Technometrics, 12, 591-612. Meyler, A, Kenny G. and Quinn T. (1998) : “Forecasting Irish Inflation using ARIMA models”. Central Bank of Ireland Technical Paper 3/RT/98. Michael, Y., 2008, ‘The Relationship between Inflation and Unemployment in Ethiopia’. M.A. dissertation, Department of Economics. Addis Ababa University. MoFED (2010): “National Economic Accounts Directorate”. Ministry of Finance and Economic Development, November 2010, Addis Ababa. Mohsin S. khan and Axel Schimmelpfenning (2006).Inflation in Pakistan: Money or Wheat? IMFWP/06/160 Muche N. (2007): “Macroeconomic Policy, Inflation and Output in Ethiopia,” A paper Presented at a workshop organized by MoFED in collaboration with the Resident Rep-resentative Office of the IMF on February 28, 2007. Muhammad A. and Nousheen A. (2014): Statistical Analysis of the Factors Affecting Inflation in Pakistan. Middle-East Journal of Scientific Research 21 (1): 181-189, 2014 World Bank (2012): “Economic Overview of Ethiopia”, available at
Yohannes A. B. ,Josef,L. and Dick D. (2009):“Inflation Dynamics and Food Prices in an Agricultural Economy: The Case of Ethiopia’’. Working paper in Economics, No 347.


Assistant Professor of Statistics, Department of Statistics, College of Natural and Com-putational Science, Wollega University, Ethiopia.

Department of Mathematics, Faculty of Natural and Computational Science, Mettu Uni-versity, Ethiopia.

Department of Mathematics, College of Natural and Computational Science, Wollega University, Ethiopia.

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