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Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy

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  • Abdullah Ghazo

Abstract

Gross Domestic Product (GDP) and consumer price index (CPI) are significant indicators to describe and evaluate economic activity and levels of development. They are also often used by decision makers so as to plan economic policy. This paper aims at modeling and predicting GDP and CPI in Jordan. In order to achieve this goal, the study applied the Box- Jenkins (JB) methodology for the period 1976-2019. Based on the results, ARIMA (3,1,1) found to be the best model for the GDP. In addition, ARIMA (1,1,0) was the best model for forecasting the CPI. The results were supported with the findings of the stationarity and identification rules test of time series under using AIC and SIC criterion. The forecasted values of the GDP and the CPI for the next three years (2020-2022) were (29342.12, 32095.10, 35106.36 million JD) and (128.31, 133.28, 139.28) respectively. Compared with 2019, the GDP is forecasted to decrease in 2020, while the CPI is forecasted to increase in 2020. This implies that the Jordanian economy is tending toward stagflation. After 2020, both GDP and CPI increased, which indicates that Jordanian economy is tending toward cost-push inflation.

Suggested Citation

  • Abdullah Ghazo, 2021. "Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 70-77, May.
    • Handle: RePEc:jfr:ijfr11:v:12:y:2021:i:3:p:70-77
    DOI: 10.5430/ijfr.v12n3p70
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    References listed on IDEAS

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    1. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    2. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(54), pages 5802-5816.
    3. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of Consumer Price Index (CPI) in Germany," MPRA Paper 92442, University Library of Munich, Germany.
    4. Stephen G. Cecchetti, 1995. "Inflation Indicators and Inflation Policy," NBER Chapters, in: NBER Macroeconomics Annual 1995, Volume 10, pages 189-236, National Bureau of Economic Research, Inc.
    5. Michail Kiriakidis & Antonios Kargas, 2013. "Greek GDP forecast estimates," Applied Economics Letters, Taylor & Francis Journals, vol. 20(8), pages 767-772, May.
    6. Nyoni, Thabani, 2019. "Forecasting UK consumer price index using Box-Jenkins ARIMA models," MPRA Paper 92410, University Library of Munich, Germany.
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    Cited by:

    1. Rasha Istaiteyeh, 2024. "Short-and Long-run Influence of COVID-19 on Jordan's Economy," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 14(1), pages 1-1.
    2. Abir HASSAN & Mahbubul Md. ALAM & Azmaine FAEIQUE, 2023. "Forecasting Monthly Inflation in Bangladesh: A Seasonal Autoregressive Moving Average (SARIMA) Approach," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 7(2), pages 25-43.
    3. Khondokar Jilhajj, 2023. "Forecasting Lending Interest Rate and Deposit Interest Rate of Bangladesh Using the Autoregressive Integrated Moving Average Model," International Journal of Economics and Financial Issues, Econjournals, vol. 13(3), pages 169-177, May.
    4. Md Ariful Haque & Aziz Ahmed, 2024. "Time Series Modeling and Forecasting on GDP Data of Bangladesh: An Application of Arima Model," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 13(4), pages 199-207, April.

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