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Modeling and forecasting inflation in Lesotho using Box-Jenkins ARIMA models

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  • NYONI, THABANI

Abstract

This research uses annual time series data on inflation rates in Lesotho from 1974 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that L is I(1). The study presents the ARIMA (0, 1, 2). The diagnostic tests further imply that the presented optimal ARIMA (0, 1, 2) model is stable and acceptable for predicting inflation in Lesotho. The results of the study apparently show that L will be approximately 5.2% over the out-of-sample forecast period. The CBL is expected to tighten Lesotho’s monetary policy in order to maintain price stability.

Suggested Citation

  • Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Lesotho using Box-Jenkins ARIMA models," MPRA Paper 92428, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:92428
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    References listed on IDEAS

    as
    1. Christian Buelens, 2012. "Inflation forecasting and the crisis: assessing the impact on the performance of different forecasting models and methods," European Economy - Economic Papers 2008 - 2015 451, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    2. Mohamed Fenira, 2014. "Democracy: A Determinant Factor in Reducing Inflation," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 363-375.
    3. Nyoni, Thabani, 2018. "Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach," MPRA Paper 88622, University Library of Munich, Germany, revised 19 Aug 2018.
    4. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    5. Nyoni, Thabani, 2018. "Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach," MPRA Paper 88132, University Library of Munich, Germany.
    6. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecasting; Inflation;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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