Two-step estimation in linear regressions with adaptive learning

• Tác giả: Alexander Mayer
• Ngôn ngữ: eng
• Ký hiệu phân loại: 526.5 Mathematical geography
• Thông tin xuất bản: 2022
• Bộ sưu tập: Báo, Tạp chí
• ID: 194900

Tóm tắt

Weak consistency and asymptotic normality of the ordinary least-squares estimator in a linear regression with adaptive learning is derived when the crucial, so-called, `gain' parameter is estimated in a first step by nonlinear least squares from an auxiliary model. The singular limiting distribution of the two-step estimator is normal and in general affected by the sampling uncertainty from the first step. However, this `generated-regressor' issue disappears for certain parameter combinations.