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DOĞRUSAL OLMAYAN OTOREGRESİF ZAMAN SERİLERİ MODELLERİNİN KESTİRİMİ

NONLINEAR TIME SERIES MODELS PREDICTING AUTOREGRESSIVE

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Abstract (2. Language): 
It is known that parametric and nonparametric methods are used for nonlinear time series. Of the parametric methods, autoregressive (AR) model and self-threshold value (SETAR) model and, of the nonparametric methods, additive regression model (ARM) have been used in this study. Nonparametric regression techniques are often sensitive to presence of otocorrelation in errors. Practical results of this sensitivity is explanied by appropriate selection of smoothing parameter. In this context, backfittting algorithm based on smoothing spline method in the existing literature is discussed. As an application, an appropriate model for the export unit value index data for Turkey is try to be determined by fitting each of AR, SETAR and, ARM models to the data.
Abstract (Original Language): 
Doğrusal olmayan zaman serileri için parametrik ve parametrik olmayan yöntemlerin kullanıldığı bilinmektedir. Bu çalışmada, parametrik yöntemlerden otoregresif (AR) ve kendinden eşik değerli (SETAR) modelleri, parametrik olmayan yöntemlerden ise toplamsal regresyon modeli (ARM) kullanılmıştır. Parametrik olmayan regresyon teknikleri hatalardaki otokorelasyonun varlığına genellikle duyarlıdırlar. Bu duyarlılığın pratik sonuçları düzeltme parametresinin uygun seçimiyle açıklanır. Bu bağlamda mevcut literatürdeki splayn düzeltme yöntemini esas alan backfitting algoritması incelenmiştir. Bu amaçla, Türkiye’deki ihracat birim değer endeks verisi, AR, SETAR ve ARM modelleri ile tahmin edilerek uygun model belirlenmeye çalışılmıştır.

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