A sliced Wasserstein and diffusion approach to random coefficient models

Keunwoo Lim, Ting Ye and Fang Han

Papers from arXiv.org

Abstract: We propose a new minimum-distance estimator for linear random coefficient models. This estimator integrates the recently advanced sliced Wasserstein distance with the nearest neighbor methods, both of which enhance computational efficiency. We demonstrate that the proposed method is consistent in approximating the true distribution. Additionally, our formulation encourages a diffusion process-based algorithm, which holds independent interest and potential for broader applications.

Date: 2025-02
New Economics Papers: this item is included in nep-dcm
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