Comparison of estimators in regression models with AR (1)‎ and AR (2)‎ disturbances : when is OLS efficient ?

المؤلف

Safi, Samir Khalid Husayn

المصدر

الاستثمار و التمويل في فلسطين بين آفاق التنمية و التحديات المعاصرة : المؤتمر العلمي لكلية التجارة المنعقد بكلية التجارة بالجامعة الإسلامية في الفترة من 8-10-2005

الناشر

الجامعة الإسلامية كلية التجارة

تاريخ النشر

2005-05-31

دولة النشر

فلسطين (قطاع غزة)

عدد الصفحات

24

التخصصات الرئيسية

الرياضيات

الملخص الإنجليزي

It is well known that the ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance and are uncorrelated.

In problems concerning time series, it is often the case that the disturbances are, in fact, correlated.

It is known that OLS may not be optimal in this context.

We consider the robustness of various estimators, including estimated generalized least squares.

We found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors.

For other forms of the dependent variable, we have developed rules of thumb to guide practitioners in their choice of estimators.

Keywords: Autoregressive; Disturbances; Ordinary Least Squares; Generalized Least Squares; Relative Efficiency.

نوع البيانات

أوراق مؤتمرات

رقم السجل

BIM-559783

• APA
• MLA
• AMA