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Modeling inflation rate factors on present consumption price index in Ethiopia: threshold autoregressive models approach

Author

Listed:
  • Alebachew Abebe

    (Haramaya University)

  • Aboma Temesgen

    (Haramaya University)

  • Belete Kebede

    (South National Regional State)

Abstract

Background Inflation is the industrious and non-stop ascent in the overall prices of any given commodity in an economy. During the global food crisis, Ethiopia experienced an unprecedented increase in inflation ranked the highest in Africa. It is among the most macroeconomic variable described nonlinear behavior. Objective The main purpose of this study was intended to modeling inflation rate factors on present consumption price index (CPI) in Ethiopia: using the threshold autoregressive (TAR) models. Methods The study was utilized the secondary data collected from monthly data of CPI for inflation rate from January 1994 to December 2020 which was obtained from central statistical Agency. The forecast was applied between the nonlinear and linear ARMA models using different techniques. The unit root test of Dickey–Fuller test was made for each variables and applied lag length transformation for the variables that had unit root. A threshold autoregressive models was utilized for data handling technique using least square estimation. Results The results showed that monthly rate of inflation was characterized a non-constant mean and an unstable variance. The outcome of Tsay tests was revealed that non linearity of CPI and SETAR(2,4,4) had the smallest value of AIC under this study. The forecasting performance comparison results were showed that the nonlinear SETAR model outperform the linear ARMA models. Moreover, the out-of-sample forecast indicates that the CPI of inflation has almost a constant trend. The in-sample forecast using the best-fit asymmetric for the SETAR(2,4,4) model the CPI series exhibits an upward trade until 2012; decreases until 2011; slightly increase up to 2018 and then decrease at the end of the study period. Conclusion The superiority in performance of nonlinear models was attributed to their ability to capture the stochastic nature of the monthly rates as evident in the pattern of the forecast errors. The investigators are recommended that using TAR models policy makers can be able to capture the price volatility persistence and also forecasting can be made.

Suggested Citation

  • Alebachew Abebe & Aboma Temesgen & Belete Kebede, 2023. "Modeling inflation rate factors on present consumption price index in Ethiopia: threshold autoregressive models approach," Future Business Journal, Springer, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:spr:futbus:v:9:y:2023:i:1:d:10.1186_s43093-023-00241-0
    DOI: 10.1186/s43093-023-00241-0
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    References listed on IDEAS

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    1. Michael T. Kiley & John M. Roberts, 2017. "Monetary Policy in a Low Interest Rate World," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 48(1 (Spring), pages 317-396.
    2. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091.
    3. George Kapetanios & Yongcheol Shin, 2006. "Unit root tests in three-regime SETAR models," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 252-278, July.
    4. Mehmet Caner & Bruce E. Hansen, 2001. "Threshold Autoregression with a Unit Root," Econometrica, Econometric Society, vol. 69(6), pages 1555-1596, November.
    5. Nicas Yabu & Nicholaus J. Kessy, 2015. "Appropriate Threshold Level of Inflation for Economic Growth: Evidence from the Three Founding EAC Countries," Applied Economics and Finance, Redfame publishing, vol. 2(3), pages 127-144, August.
    6. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
    8. Galyna Grynkiv & Lars Stentoft, 2018. "Stationary Threshold Vector Autoregressive Models," JRFM, MDPI, vol. 11(3), pages 1-23, August.
    9. Jiranyakul, Komain, 2020. "Government Expenditures and Economic Growth: A Cointegration Analysis for Thailand under the Floating Exchange Rate Regime," MPRA Paper 109585, University Library of Munich, Germany.
    10. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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