Author(s):
Gemechu Bekana Fufa, Abebe Legese
Email(s):
gemechu.bekana@yahoo.com
DOI:
10.5958/2321-581X.2020.00024.0 
Address:
Gemechu Bekana Fufa1, Abebe Legese2
1Assistant Professor of Statistics, Department of Statistics, College of Natural and Computational Science, Wollega University, Ethiopia.
2Lecturer of Mathematics, Department of Mathematics, College of Natural and Computational Science, Wollega University, Ethiopia.
*Corresponding Author
Published In:
Volume - 11,
Issue - 3,
Year - 2020
ABSTRACT:
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 aim of this study is to assess the determinants of inflation by using regression models in Ethiopia. Multiple regression Models, Logistic regression 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. 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.
Cite this article:
Gemechu Bekana Fufa, Abebe Legese. Assessing the Determinants of Inflation in Ethiopia: Regression Models Application. Research J. Engineering and Tech. 2020;11(3):138-145. doi: 10.5958/2321-581X.2020.00024.0
Cite(Electronic):
Gemechu Bekana Fufa, Abebe Legese. Assessing the Determinants of Inflation in Ethiopia: Regression Models Application. Research J. Engineering and Tech. 2020;11(3):138-145. doi: 10.5958/2321-581X.2020.00024.0 Available on: https://ijersonline.org/AbstractView.aspx?PID=2020-11-3-3
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