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11 avril 2022
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- 2204.05298
- doi: 10.1016/j.spl.2022.109761
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Alexander Mayer, « Two-step estimation in linear regressions with adaptive learning », arXiv - économie, ID : 10.1016/j.spl.2022.109761
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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.