Modeling and Forecasting Inflation in Ethiopia Using Multivariate Time Series Analysis
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Sedik Ali
Inflation is a fundamental measure of macroeconomic stability that negatively impacts the nation's economy and affects many other macroeconomic variables. Thus, in this study, the major objective was based on modeling and forecasting inflation in Ethiopia and its components using a VAR model. The analysis was based on annual data from 1992 to 2021, encompassing 30 years. The results indicated that all five series were non-stationary at the level but stationary after their first differencing at a 5% level of significance. Johansen's cointegration tests were conducted. The results indicated the presence of at least one co-integration relationship between the variables. The Vector Error Correction Model (VECM) was fitted to model short run and long run relationships among inflation and other macro-econometric series such as GDP growth, government expenditure, money supply, and imports, and the result indicated that the coefficient of error correction term is negative (-0.279), which indicated that the fitted VECM model continues to move toward long run equilibrium and converges. Granger causality tests were employed to explore potential causal relationships. Impulse response analysis and variance decomposition were used to determine the short-run interactions among the variables. Finally, using the fitted model, out-of-sample forecasts were produced, yielding forecasted plots and values for the endogenous variables. According to the forecasted inflation rates for the next five years, prices are projected to decrease by approximately 18.0% in 2022, 6.8% in 2023, and then increase by approximately 8.3% in 2024. Subsequently, prices are expected to decrease by approximately 2% in 2025 and 5% in 2026 over the specified time periods.
Amisi, B., & Ouma, P. (2021). Forecasting Kenya’s inflation rate using a VARMA for price of imported crude oil and Kenya’s previous inflation rate time series. Maseno university.
Ankit, S. (2011). Factors Influencing Online Banking Customer Satisfaction and Their Importance in Improving Overall Retention Levels : An Indian Banking Perspective. Information and Knowledge Management, 1(1), 45–55.
Danoro, D. D. (2020). Modeling And Forecasting Volatility Of Price Inflation In Ethiopia Using GARCH Family Models. 4(1), 10–11
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.1080/01621459.1979.10482531
Fischer, S., & Sahay, R. (2000). IMF Working Paper INTERNATIONAL MONETARY FUND. WP/06/212, Asian Pacific Department.
Fischer, S. (1993). deficits also reduce both capital accumulation and productivity growth . Examination of. NBER Working Paper Series, 4565, 36.
Hamilton, J. D., & Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64(1–2), 307–333. https://doi.org/10.1016/0304-4076(94)90067-1
Ndiaye, C. T., & Konte, M. (2017). The Role of Inflation Uncertainty on Economic Growth : The Case of the WAEMU The Role of Inflation Uncertainty on Economic Growth : The Case of the WAEMU Cheikh Tidiane Ndiaye * and Mamadou Abdoulaye Konte. January, 1–11.
Phillips, P., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression Author ( s ): Peter C . B . Phillips and Pierre Perron Published by : Oxford University Press on behalf of Biometrika Trust Stable URL : https://www.jstor.org/stable/2336182 REFERENCES Linked references ar. 75(2), 335–346.
Lütkepohl, H. (2005). New introduction to multiple time series analysis. New Introduction to Multiple Time Series Analysis, 1–764. https://doi.org/10.1007/978-3-540-27752-1
