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.
Two-step estimation in linear regressions with adaptive learning
Mayer, Alexander
2023-01-01
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.File in questo prodotto:
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