Basit Doğrusal Otoregresif Modeller Sisteminde Parametre Tahmini ve Hipotez Testi: Simetrik İnovasyonlar

Bu çalışmada, simetrik dağılıma sahip hata terimli basit otoregresif modeller sistemi incelenerek normal dağılım varsayımının geçersiz olduğu durumlardaki metodoloji birden fazla bağımsız bilgi kaynağı olduğu durumlara genellenmiş, uyarlanmış en çok olabilirlik yöntemi ile tahmin ediciler elde edilmiş ve parametre vektörünün tüm kaynaklar için değişip değişmediğini test edecek sağlam ve etkin test istatistikleri geliştirilmiştir. Elde edilen tahmin ediciler ve test istatistikleri bu alandaki uygulamalarda sıkça kullanılan en küçük kareler yöntemi ile elde edilen tahmin edici ve test istatistikleri ile karşılaştırılmış ve daha iyi sonuçlar verdiği görülmüştür.

Anahtar Kelimeler:

En Çok Olabilirlik Tahmin Edicileri, En Küçük Kareler Tahmin Edicileri, Uyarlanmış En Çok Olabilirlik Tahmin Edicileri, Otoregresyon, Sağlamlık, Simetrik Dağılımlar

Estimation of Parameters and Hypothesis Testing in the System of Simple Autoregressive Models: Symmetric Innovations

In this study, the simple autoregressive models system having symmetric innovations have been investigated and the methodology under non-normality has been extended to various independent sources of information. Modified likelihood estimators are obtained; robust and efficient statistics for testing whether the parameter vector remains the same from one source to another are developed. The estimators and the test statistics obtained are compared to the corresponding least squares statistics which are widely used in this context in applications, and found to give more accurate results.

Keywords:

Maximum Likelihood Estimators, Least Squares Estimators, Modified Likelihood Estimators, Autoregression, Robustness, Non-Normality,

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