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Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach

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  • NYONI, THABANI

Abstract

Of uttermost importance is the fact that forecasting macroeconomic variables provides a clear picture of what the state of the economy will be in future (Sultana et al, 2013). Nothing is more important to the conduct of monetary policy than understanding and predicting inflation (Kohn, 2005). Inflation is the scourge of the modern economy and is feared by central bankers globally and forces the execution of unpopular monetary policies. Inflation usually makes some people unfairly rich and impoverishes others and therefore it is an economic pathology that stands in the way of any sustainable economic growth and development. Models that make use of GARCH, as highlighted by Ruzgar & Kale (2007); vary from predicting the spread of toxic gases in the atmosphere to simulating neural activity but Financial Econometrics remains the leading discipline and apparently dominates the research on GARCH. The main objective of this study is to model monthly inflation rate volatility in Zimbabwe over the period July 2009 to July 2018. Our diagnostic tests indicate that our sample has the characteristics of financial time series and therefore, we can employ a GARCH – type model to model and forecast conditional volatility. The results of the study indicate that the estimated model, the AR (1) – GARCH (1, 1) model; is indeed an AR (1) – IGARCH (1, 1) process and is not only appropriate but also the best. Since the study provides evidence of volatility persistence for Zimbabwe’s monthly inflation data; monetary authorities ought to take into cognisance the IGARCH behavioral phenomenon of monthly inflation rates in order to design an appropriate monetary policy.

Suggested Citation

  • Nyoni, Thabani, 2018. "Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach," MPRA Paper 88132, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:88132
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    References listed on IDEAS

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    Cited by:

    1. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Lesotho using Box-Jenkins ARIMA models," MPRA Paper 92428, University Library of Munich, Germany.
    2. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of Consumer Price Index (CPI) in Germany," MPRA Paper 92442, University Library of Munich, Germany.
    3. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of inflation in Egypt (1960-2017)," MPRA Paper 92446, University Library of Munich, Germany.
    4. Nyoni, Thabani, 2019. "Modeling and forecasting CPI in Iran: A univariate analysis," MPRA Paper 92454, University Library of Munich, Germany.
    5. Nyoni, Thabani, 2019. "Demystifying inflation dynamics in Rwanda: an ARMA approach," MPRA Paper 93982, University Library of Munich, Germany.
    6. Nyoni, Thabani & Mutongi, Chipo & Nyoni, Munyaradzi & Hamadziripi, Oscar Hapanyengwi, 2019. "Understanding inflation dynamics in the Kingdom of Eswatini: a univariate approach," MPRA Paper 93979, University Library of Munich, Germany.
    7. Nyoni, Thabani, 2019. "Predicting inflation in the Kingdom of Bahrain using ARIMA models," MPRA Paper 92452, University Library of Munich, Germany.
    8. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Philippines using ARIMA models," MPRA Paper 92429, University Library of Munich, Germany.
    9. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Burundi using ARIMA models," MPRA Paper 92444, University Library of Munich, Germany.
    10. Nyoni, Thabani & Mutongi, Chipo, 2019. "Modeling and forecasting inflation in The Gambia: an ARMA approach," MPRA Paper 93980, University Library of Munich, Germany.
    11. Nyoni, Thabani, 2019. "Understanding inflation patterns in Thailand: An ARMA approach," MPRA Paper 92451, University Library of Munich, Germany.
    12. Hapanyengwi, Hamadziripi Oscar & Mutongi, Chipo & Nyoni, Thabani, 2019. "Understanding Inflation Dynamics in the kingdom of Eswantini: A Univariate Approach," MPRA Paper 94560, University Library of Munich, Germany, revised 18 Jun 2019.
    13. Nyoni, Thabani, 2019. "Inflation dynamics in Jamaica: Evidence from the ARMA methodology," MPRA Paper 92449, University Library of Munich, Germany.
    14. Nyoni, Thabani, 2019. "Understanding inflation dynamics in the United States of America (USA): A univariate approach," MPRA Paper 92460, University Library of Munich, Germany.
    15. Nyoni, Thabani, 2019. "Prediction of Inflation in Algeria using ARIMA models," MPRA Paper 92426, University Library of Munich, Germany.
    16. Nyoni, Thabani, 2019. "Predicting inflation in Senegal: An ARMA approach," MPRA Paper 92431, University Library of Munich, Germany.
    17. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Tanzania using ARIMA models," MPRA Paper 92458, University Library of Munich, Germany.
    18. Nyoni, Thabani, 2019. "Inflation dynamics in Niger unlocked: An ARMA approach," MPRA Paper 92450, University Library of Munich, Germany.
    19. Nyoni, Thabani, 2019. "Uncovering inflation dynamics in Morocco: An ARIMA approach," MPRA Paper 92455, University Library of Munich, Germany.
    20. Nyoni, Thabani, 2019. "Predicting inflation in Sri Lanka using ARMA models," MPRA Paper 92432, University Library of Munich, Germany.
    21. Nyoni, Thabani, 2019. "Understanding inflation trends in Finland: A univariate approach," MPRA Paper 92448, University Library of Munich, Germany.
    22. Nyoni, Thabani, 2019. "Understanding inflation trends in Israel: A univariate approach," MPRA Paper 92427, University Library of Munich, Germany.
    23. Nyoni, Thabani, 2019. "Forecasting inflation in Burkina Faso using ARMA models," MPRA Paper 92443, University Library of Munich, Germany.

    More about this item

    Keywords

    ARCH; Forecasting; GARCH; IGARCH; Inflation Rate Volatility; Zimbabwe;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G0 - Financial Economics - - General

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