Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model

Tao Yu, Pengfei Li, Baojiang Chen, Ao Yuan and Jing Qin

Journal of Econometrics, 2023, vol. 235, issue 2, 454-469

Abstract: In this paper, we study the linear transformation model in a general setup. This model includes many important and popular models in statistics and econometrics as special cases. Although it has been studied for many years, the methods in the literature are based on kernel-smoothing techniques or make use of only the ranks of the responses in the estimation of the parametric components. The former approach needs a tuning parameter, which is not easily optimally specified in practice; and some of the latter may be computationally expensive. In this paper, we propose two methods: a pairwise rank likelihood method and an estimation-equation-based method motivated from the score function of this pairwise rank likelihood. We also explore the theoretical properties of the proposed estimators. Via extensive numerical studies, we demonstrate that our methods are appealing in that the estimators are not only robust to the distribution of the random errors but also lead to mean square errors that are in many cases comparable to or smaller than those of existing methods.

Keywords: Linear transformation model; M-estimation; Profile likelihood; Pairwise rank likelihood; Pseudo-likelihood; Semiparametric inference (search for similar items in EconPapers)
JEL-codes: C13 C14 C18 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:454-469

DOI: 10.1016/j.jeconom.2022.05.003

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