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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5011100
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