Two-step estimation in linear regressions with adaptive learning

Alexander Mayer

Papers from arXiv.org

Abstract: 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.

Date: 2022-04, Revised 2022-11
New Economics Papers: this item is included in nep-ecm and nep-ets
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