In this study the methods that are used to estimate seemingly unrelated regression equations, when each of the equations includes first-order autocorrelation, are examined.
📌 3 Key Takeaways
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Objective & Hypothesis:When the errors of seemingly unrelated regression equations are both contemporaneously and includes autocorrelation, it is necessary to avoid the reduction of efficiency in estimating the parameters.
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Key Findings & Data:When the errors of seemingly unrelated regression equations are both contemporaneously and includes autocorrelation, it is necessary to avoid the reduction of efficiency in estimating the parameters.
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Conclusion & Impact:In this study the methods that are used to estimate seemingly unrelated regression equations, when each of the equations includes first-order autocorrelation, are examined.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
When the errors of seemingly unrelated regression equations are both contemporaneously and includes autocorrelation, it is necessary to avoid the reduction of efficiency in estimating the parameters. In this study the methods that are used to estimate seemingly unrelated regression equations, when each of the equations includes first-order autocorrelation, are examined.
Özet
Görünüşte ilişkisiz regresyon denklemlerine ilişkin hataların hem aynı zamanda ilişkili hemde otokorelasyonlu olduğu durumda parametre tahminlerinde etkinlik kaybını önlemek gerekir. Çalışmada herbir denklemin birinci dereceden otokorelasyonu içermesi durumunda görünüşte ilişkisiz regresyon denklemlerinin kestiriminde kullanılan yöntemler incelenmiştir.
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Cite This Article
AĞAOĞLU, E., UZGÖREN, N. (2022). Estimation of seemingly unrelated regression equations that include autocorrelation. Anadolu Üniversitesi Fen Fakültesi Dergisi, Cilt: 0 - Sayı: 4, 53–64. https://ajindex.com/articles/438658