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

Nazif Catik () and Mehmet Karacuka

No 20, DICE Discussion Papers from Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE)

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.

Keywords: Inflation forecasting; Neural networks; Unobserved components model (search for similar items in EconPapers)
JEL-codes: C45 C53 E31 E37 (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-ara, nep-cba, nep-cmp, nep-cwa, nep-ets, nep-for and nep-mon
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Citations: View citations in EconPapers (4)

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