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Inflation forecasting and the crisis: assessing the impact on the performance of different forecasting models and methods

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  • Christian Buelens

Abstract

This paper analyses how euro area inflation forecasts have been affected by the financial and economic crisis. Its first objective is to evaluate the accuracy of three representative groups of inflation forecasting models (rules of thumb and benchmark models; autoregressive moving average models; autoregressive distributed lag models) under a direct and an indirect approach, respectively. The second objective of the paper is to study how the absolute and relative forecasting performances of the models and approaches have been impacted by the economic and financial crisis. The paper finds that direct forecasting models selected on the basis of a penalty function generally dominate simple benchmark models. The analysis furthermore suggests that when an appropriate specification for the component-specific models is found, indirect forecasts outperform the corresponding direct forecasts. Nonetheless, in line with the findings from earlier studies, there are insufficient elements to assert a systematic superiority of one of the two approaches. Concerning the second objective, the across-the-board rise in the forecast errors of all models considered, confirms that inflation forecasting has become substantially more difficult after the onset of the crisis. However, the deterioration of the different models has been uneven: indeed, direct autoregressive distributed lag models and indirect models improved in relative terms during the crisis.

Suggested Citation

  • 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.
  • Handle: RePEc:euf:ecopap:0451
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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. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Division of Economics, School of Business, University of Leicester.
    3. Nyoni, Thabani & Mutongi, Chipo, 2019. "Modeling and forecasting inflation in The Gambia: an ARMA approach," MPRA Paper 93980, University Library of Munich, Germany.
    4. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of inflation in Egypt (1960-2017)," MPRA Paper 92446, University Library of Munich, Germany.
    5. Nyoni, Thabani, 2019. "Understanding inflation patterns in Thailand: An ARMA approach," MPRA Paper 92451, University Library of Munich, Germany.
    6. Nyoni, Thabani, 2019. "Prediction of Inflation in Algeria using ARIMA models," MPRA Paper 92426, University Library of Munich, Germany.
    7. Charemza, Wojciech & Díaz, Carlos & Makarova, Svetlana, 2019. "Quasi ex-ante inflation forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 35(3), pages 994-1007.
    8. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Tanzania using ARIMA models," MPRA Paper 92458, University Library of Munich, Germany.
    9. Nyoni, Thabani, 2019. "Demystifying inflation dynamics in Rwanda: an ARMA approach," MPRA Paper 93982, University Library of Munich, Germany.
    10. 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.
    11. Nyoni, Thabani, 2019. "Uncovering inflation dynamics in Morocco: An ARIMA approach," MPRA Paper 92455, University Library of Munich, Germany.
    12. Svetlana Makarova, 2016. "ECB footprints on inflation forecast uncertainty," Bank of Estonia Working Papers wp2016-5, Bank of Estonia, revised 19 Jul 2016.
    13. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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