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G7 Ülkeleri İşsizlik Oranı Tahminleri: SARIMA-GARCH Model Karşılaştırması

İşsizlik verilerinin yakın zamanda mevsimsellikten arındırılmış olarak yayınlanmış olmasına rağmen, mevsimsellik hareketli ortalama (MA) veya oto-regresif (AR) terimlerde hala var olabilir. Bu, oto-korelasyon fonksiyonu (ACF) ve kısmi ACF (PACF) diyagramlarında düzenli bir model arayarak tespit edilebilir. Bu nedenle, işsizlik oranlarını tahmin etmeyi amaçlayan modeller, daha iyi ortalama denklem tahminleri elde etmek için mevsimsellik özelliklerini dikkate almalıdır. Tek değişkenli modeller çoğunlukla entegre ARMA (ARIMA) veya genelleştirilmiş oto-regresif heteroskedastik (GARCH) modelleri veya bunların herhangi bir kombinasyonunu kullanır. Ortalama denklemler daha iyi yapılandırıldıktan sonra, GARCH varyans denklemi tahminlerinin tahminlerde daha doğru sonuçlar vermesi beklenir. Bu çalışmada ilk olarak, 1995-2019 dönemi için G-7 ülkelerindeki mevsimsellikten arındırılmış işsizlik oranı verilerinin ACF'leri ve PACF'leri incelenmektedir. Daha sonra, GARCH'ın mevsimsel ARIMA (SARIMA) bağlı oynaklık modellerinin ortalama, mutlak değer GARCH, GJR-GARCH, üstel GARCH ve asimetrik GARCH modellerinin 4 çeyrek ve 8 çeyrek ileriye dönük tahmin performansını karşılaştırır. Bu modellerin performansı da SARIMA ve MA filtreli volatilite modelleriyle karşılaştırılmıştır. Sonuçlar, mevsimselliğin mevsimsellikten arındırılmış işsizlik verilerinde bile yeniden incelenmesi gerektiğini göstermektedir, çünkü SARIMA modelleri örneklem dışı tahmin hataları açısından ARIMA modellerinden daha iyi performans göstermektedir. SARIMA-GARCH modellerinin yanı sıra daha iyi örneklem dışı tahmin doğruluğu sağlar.

Anahtar Kelimeler:

mevsimsellik, SARIMA, GARCH, tahmin, işsizlik

G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models

Keywords:

seasonality, garch, sarima, unemployment, forecast,

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