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A comparative analysis of alternative univariate time series models in forecasting Turkish inflation

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  • Catik, A. Nazif
  • Karaçuka, Mehmet

Abstract

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.

Suggested Citation

  • Catik, A. Nazif & Karaçuka, Mehmet, 2011. "A comparative analysis of alternative univariate time series models in forecasting Turkish inflation," DICE Discussion Papers 20, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    • Handle: RePEc:zbw:dicedp:20
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    References listed on IDEAS

    as
    1. Frederic S. Mishkin, 2000. "Inflation Targeting in Emerging-Market Countries," American Economic Review, American Economic Association, vol. 90(2), pages 105-109, May.
    2. Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
    3. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Gu, Yiquan & Wenzel, Tobias, 2012. "Transparency, entry, and productivity," Economics Letters, Elsevier, vol. 115(1), pages 7-10.
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    3. Clémence Christin, 2013. "Entry Deterrence Through Cooperative R&D Over-Investment," Recherches économiques de Louvain, De Boeck Université, vol. 79(2), pages 5-26.
    4. Haucap, Justus & Herr, Annika & Frank, Björn, 2011. "In vino veritas: Theory and evidence on social drinking," DICE Discussion Papers 37, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).

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    More about this item

    Keywords

    Inflation forecasting; Neural networks; Unobserved components model;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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

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