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


  • Catik, A. Nazif
  • Karaçuka, Mehmet


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, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  • Handle: RePEc:zbw:dicedp:20

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    References listed on IDEAS

    1. Blomstrom, Magnus & Kokko, Ari, 1998. " Multinational Corporations and Spillovers," Journal of Economic Surveys, Wiley Blackwell, vol. 12(3), pages 247-277, July.
    2. Blomström, Magnus & Kokko, Ari, 2003. "The Economics of Foreign Direct Investment Incentives," EIJS Working Paper Series 168, Stockholm School of Economics, The European Institute of Japanese Studies.
    3. Javorcik, Beata Smarzynska & Spatareanu, Mariana, 2008. "To share or not to share: Does local participation matter for spillovers from foreign direct investment?," Journal of Development Economics, Elsevier, vol. 85(1-2), pages 194-217, February.
    4. Magnus Blomstrom & Robert E. Lipsey & Mario Zejan, 1992. "What Explains Developing Country Growth?," NBER Working Papers 4132, 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.
    2. Haucap, Justus & Herr, Annika & Frank, Björn, 2011. "In vino veritas: Theory and evidence on social drinking," DICE Discussion Papers 37, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    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. Stühmeier Torben & Wenzel Tobias, 2012. "Regulating Advertising in the Presence of Public Service Broadcasting," Review of Network Economics, De Gruyter, vol. 11(2), pages 1-23, June.

    More about this item


    Inflation forecasting; Neural networks; Unobserved components model;

    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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