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Understanding inflation trends in Israel: A univariate approach




This paper uses annual time series data on inflation in Israel from 1960 to 2017, to model and forecast inflation using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that Q is I (1). The study presents the ARIMA (1, 1, 2) model for predicting inflation in Israel. The diagnostic tests further show that the presented parsimonious model is stable and acceptable for predicting inflation in Israel. The results of the study apparently show that inflation in Israel is likely to be hovering around 1.6% over the next decade. Basically, the study encourages the Bank of Israel to continue being transparent and independent in order to retain credibility and boost its ability to engineer successful macroeconomic policy actions.

Suggested Citation

  • Nyoni, Thabani, 2019. "Understanding inflation trends in Israel: A univariate approach," MPRA Paper 92427, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:92427

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    References listed on IDEAS

    1. repec:boi:isrerv:v:1:y:2003:i:1:p:1-10 is not listed on IDEAS
    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. Anders Bredahl Kock & Timo Teräsvirta, 2013. "Forecasting the Finnish Consumer Price Inflation Using Artificial Neural Network Models and Three Automated Model Selection Techniques," Finnish Economic Papers, Finnish Economic Association, vol. 26(1), pages 13-24, Spring.
    4. Leonardo Leiderman & Hadas Bar-Or, 2000. "Monetary Policy Rules and Transmission Mechanisms Under Inflation Targeting in Israel," Working Papers Central Bank of Chile 71, Central Bank of Chile.
    5. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    6. Nyoni, Thabani, 2018. "Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach," MPRA Paper 88132, University Library of Munich, Germany.
    7. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
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    More about this item


    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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