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Understanding inflation trends in Finland: A univariate approach

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  • NYONI, THABANI

Abstract

This research uses annual time series data on inflation rates in Finland from 1960 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that F is I(1). The study presents the ARIMA (1, 1, 3) model. The diagnostic tests further imply that the presented optimal ARIMA (1, 1, 3) model is stable and acceptable in predicting Finnish inflation. The results of the study apparently show that F will be hovering around 1% over the next 10 years. Policy makers and the business community in Finland are expected to take advantage of the anticipated stable inflation rates over the next decade.

Suggested Citation

  • Nyoni, Thabani, 2019. "Understanding inflation trends in Finland: A univariate approach," MPRA Paper 92448, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:92448
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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. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    3. Nyoni, Thabani, 2018. "Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach," MPRA Paper 88132, University Library of Munich, Germany.
    4. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    5. Mohamed Fenira, 2014. "Democracy: A Determinant Factor in Reducing Inflation," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 363-375.
    6. 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.
    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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