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Two-step estimation in linear regressions with adaptive learning

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  • Mayer, Alexander

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.

Suggested Citation

  • Mayer, Alexander, 2023. "Two-step estimation in linear regressions with adaptive learning," Statistics & Probability Letters, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:stapro:v:195:y:2023:i:c:s0167715222002747
    DOI: 10.1016/j.spl.2022.109761
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    References listed on IDEAS

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    1. Christopeit, Norbert & Massmann, Michael, 2018. "Estimating Structural Parameters In Regression Models With Adaptive Learning," Econometric Theory, Cambridge University Press, vol. 34(1), pages 68-111, February.
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