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Forecasting the Finnish Consumer Price Inflation Using Artificial Neural Network Models and Three Automated Model Selection Techniques

Author

Listed:
  • Anders Bredahl Kock

    () (CREATES, Aarhus University)

  • Timo Teräsvirta

    () (CREATES, Aarhus University)

Abstract

This paper is concerned with forecasting the Finnish inflation rate. It is being forecast using linear autoregressive and nonlinear neural network models. Perhaps surprisingly, building the models on the nonstationary level series and forecasting with them produces forecasts with a small er root mean square forecast error than doing the same with differenced series. The paper also contains pairwise comparisons between the benchmark forecasts from linear autoregressive models and ones from neural network models using Wilcoxon’s signed-rank test.

Suggested Citation

  • 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.
  • Handle: RePEc:fep:journl:v:26:y:2013:i:1:p:13-24
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    Citations

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    Cited by:

    1. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of Consumer Price Index (CPI) in Germany," MPRA Paper 92442, University Library of Munich, Germany.
    2. Nyoni, Thabani, 2019. "Forecasting consumer price index in Norway: An application of Box-Jenkins ARIMA models," MPRA Paper 92411, University Library of Munich, Germany.
    3. Nyoni, Thabani, 2019. "Predicting inflation in the Kingdom of Bahrain using ARIMA models," MPRA Paper 92452, University Library of Munich, Germany.
    4. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Philippines using ARIMA models," MPRA Paper 92429, University Library of Munich, Germany.
    5. Nyoni, Thabani, 2019. "Predicting CPI in Singapore: An application of the Box-Jenkins methodology," MPRA Paper 92413, University Library of Munich, Germany.
    6. Nyoni, Thabani, 2019. "Understanding inflation trends in Israel: A univariate approach," MPRA Paper 92427, University Library of Munich, Germany.
    7. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski, Economic Research Department.
    8. Nyoni, Thabani, 2019. "Forecasting UK consumer price index using Box-Jenkins ARIMA models," MPRA Paper 92410, University Library of Munich, Germany.
    9. Nyoni, Thabani, 2019. "Understanding inflation dynamics in the United States of America (USA): A univariate approach," MPRA Paper 92460, University Library of Munich, Germany.
    10. Nyoni, Thabani, 2019. "Time series modeling and forecasting of the consumer price index in Japan," MPRA Paper 92409, University Library of Munich, Germany.
    11. Nyoni, Thabani, 2019. "Forecasting Australian CPI using ARIMA models," MPRA Paper 92412, University Library of Munich, Germany.
    12. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    13. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Tanzania using ARIMA models," MPRA Paper 92458, University Library of Munich, Germany.
    14. Nyoni, Thabani, 2019. "Understanding inflation trends in Finland: A univariate approach," MPRA Paper 92448, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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

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