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Multivariate Generalized Autoregressive Conditional Heteroscedasticity Modeling of the Relationship Between Major Economic Indicators in Ethiopia

Author

Listed:
  • Daba Ketema Huriso

    (Bule Hora University)

  • Belay Belete Anjullo

    (Arba Minch University)

  • Yilikal Tesfaye Haile

    (Arba Minch University)

  • Derbachew Asfaw Teni

    (Addis Ababa University)

Abstract

We model the relationship between major economic indicators in Ethiopia. We obtained 28 years of data from the National Bank of Ethiopia (NBE) for the period between 1991 and 2018, these data have been transformed into quarterly data. It was seen that there are upward trend and volatility clustering in the data. Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) models were employed for volatility modelling with the specific interest in identifying the trend and the impact of macroeconomic variables on economic growth. Results from Dynamic Conditional Correlations (DCC (1,1))-GARCH model showed the sum of ARCH and GARCH effect was 0.672. This implied volatility is neither permanent nor explosive, that is a shock to volatility in one period will not lead to greater volatility in the next period. It was also found that the estimated association parameter between consumer price index which is proxy for inflation and real gross domestic product was -0.557, this shows negative association between inflation and real gross domestic product. Furthermore; volatility on exchange rate and money supply has a significant positive association with volatility on real gross domestic product and inflation. NBE should be cautious in controlling the money supply to control the problem of inflation, since the increase in money supply increases inflation, and inflation affects economic growth.

Suggested Citation

  • Daba Ketema Huriso & Belay Belete Anjullo & Yilikal Tesfaye Haile & Derbachew Asfaw Teni, 2024. "Multivariate Generalized Autoregressive Conditional Heteroscedasticity Modeling of the Relationship Between Major Economic Indicators in Ethiopia," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 7127-7142, June.
  • Handle: RePEc:spr:jknowl:v:15:y:2024:i:2:d:10.1007_s13132-023-01422-6
    DOI: 10.1007/s13132-023-01422-6
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    References listed on IDEAS

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    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    4. Dipendra Sinha, 2007. "Effects of Volatility of Exports in the Philippines and Thailand," The IUP Journal of Financial Economics, IUP Publications, vol. 0(3), pages 78-83, September.
    5. Khan, Abdul Aleem & Ahmed, Qazi Masood & Hyder, Kalim, 2007. "Determinants oF Recent Inflation in Pakistan," MPRA Paper 16254, University Library of Munich, Germany, revised 2007.
    6. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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