کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417566 681534 2012 25 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models
چکیده انگلیسی

An approximation to order T−2T−2 is obtained for the bias of the full vector of least-squares estimates obtained from a sample of size TT in general stable but not necessarily stationary ARX(1) models with normal disturbances. This yields generalizations, allowing for various forms of initial conditions, of Kendall’s and White’s classic results for stationary AR(1) models. The accuracy of various alternative approximations is examined and compared by simulation for particular parameterizations of AR(1) and ARX(1) models. The results show that often the second-order approximation is considerably better than its first-order counterpart and hence opens up perspectives for improved bias correction. However, order T−2T−2 approximations are also found to be more vulnerable in the near unit root case than the much simpler order T−1T−1 approximations.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 11, November 2012, Pages 3705–3729
نویسندگان
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