Two-step estimation in linear regressions with adaptive learning
Econometrics
2023-01-11 v3 Applications
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.
Keywords
Cite
@article{arxiv.2204.05298,
title = {Two-step estimation in linear regressions with adaptive learning},
author = {Alexander Mayer},
journal= {arXiv preprint arXiv:2204.05298},
year = {2023}
}