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The optimal forecast model for consumer price index of Puntland State, Somalia

Author

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  • Abdullahi Osman Ali

    (Ministry of Finance)

  • Jama Mohamed

    (University of Hargeisa)

Abstract

Effective monetary and fiscal policy can be set with an appropriate inflation forecast. Therefore, the aim of this study is to forecast Puntland’s consumer price index (CPI) using monthly data from July, 2017 to February, 2021. The study adopted and compared different time series models including regression with ARIMA errors (ARIMAX), STL decomposition, robust exponential smoothing (ROBETS), single exponential smoothing (SES) and artificial neural network (ANN) models. Various forecast accuracy measures and information criteria such as Akaike Information Criteria (AIC), Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were adopted to assess the forecasting ability of these five models. The results illustrated that ANN and STL decomposition models can better forecast Puntland’s CPI. The forecast results from ANN and STL decomposition models revealed that the CPI of Puntland will slightly decline or stay constant over the forecasted period. Consistent with the result, the Ministry of Finance and the State Bank of Puntland need to keep inflation within the targeted range.

Suggested Citation

  • Abdullahi Osman Ali & Jama Mohamed, 2022. "The optimal forecast model for consumer price index of Puntland State, Somalia," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4549-4572, December.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:6:d:10.1007_s11135-022-01328-6
    DOI: 10.1007/s11135-022-01328-6
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    References listed on IDEAS

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    1. Coibion, Olivier & Gorodnichenko, Yuriy & Kumar, Saten & Pedemonte, Mathieu, 2020. "Inflation expectations as a policy tool?," Journal of International Economics, Elsevier, vol. 124(C).
    2. Robert G. Brown & Richard F. Meyer, 1961. "The Fundamental Theorem of Exponential Smoothing," Operations Research, INFORMS, vol. 9(5), pages 673-685, October.
    3. 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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