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arXiv · 1902.05261

Rate-optimal nonparametric estimation for random coefficient regression models

Hajo Holzmann·Alexander Meister
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Abstract

Random coefficient regression models are a popular tool for analyzing unobserved heterogeneity, and have seen renewed interest in the recent econometric literature. In this paper we obtain the optimal pointwise convergence rate for estimating the density in the linear random coefficient model over H\"older smoothness classes, and in particular show how the tail behavior of the design density impacts this rate. In contrast to previous suggestions, the estimator that we propose and that achieves the optimal convergence rate does not require dividing by a nonparametric density estimate. The optimal choice of the tuning parameters in the estimator depends on the tail parameter of the design density and on the smoothness level of the H\"older class, and we also study adaptive estimation with respect to both parameters.

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Hajo Holzmann, Alexander Meister. 2019-02-14. Rate-optimal nonparametric estimation for random coefficient regression models. https://arxiv.org/abs/1902.05261

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