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Title:A comparative analysis of alternative univariate time series models in forecasting Turkish inflation
Authors:Catik, A. Nazif
Karaçuka, Mehmet
Issue Date:2011
Series/Report no.:DICE discussion paper 20
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.
Subjects:Inflation forecasting
Neural networks
Unobserved components model
JEL:C45
C53
E31
E37
Document Type:Working Paper
Appears in Collections:DICE Discussion Paper, Düsseldorf Institute for Competition Economics (DICE)

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