We conclude that the combination of forecasts of the nonstationary artificial neural network and the nonstation-ary vector autoregressive model have the best one-ahead forecast performance for Turkish inflation.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper we investigate Turkish inflation forecast performance for nine alternative statistical models, particularly focusing on the effects of both linearity and stationarity.
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Key Findings & Data:Moreover, out of sample forecasts obtained through the models having minimum root mean square errors are combined with using fixed and varying weights approaches.
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Conclusion & Impact:We conclude that the combination of forecasts of the nonstationary artificial neural network and the nonstation-ary vector autoregressive model have the best one-ahead forecast performance for Turkish inflation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Inflation · forecast · VAR
Abstract
In this paper we investigate Turkish inflation forecast performance for nine alternative statistical models, particularly focusing on the effects of both linearity and stationarity. Moreover, out of sample forecasts obtained through the models having minimum root mean square errors are combined with using fixed and varying weights approaches. We conclude that the combination of forecasts of the nonstationary artificial neural network and the nonstation-ary vector autoregressive model have the best one-ahead forecast performance for Turkish inflation.
UÇAR, N., KÖSE, N., AKSOY, S. (2003). DOĞRUSAL VE DOĞRUSAL OLMAYAN MODELLERLE ENFLASYON ÖNGÖRÜSÜ: DURAĞAN VE DURAĞAN OLMAMANIN ETKİLERİ. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 5 - Sayı: 3, 45–56. https://ajindex.com/articles/14582