2 citations · 4 across the 7 of their papers we have counts for
8 papers
Testing Tail-Index Homogeneity via Exceedance-Covariate Independence
Thomas T. Yang
We propose a test of whether the tail index of a heavy-tailed outcome is homogeneous across observed covariates. The key observation is that, under tail-index homogeneity, normaliz…
On the Rank Condition of Tail Index Regressions and a Comparative Study with Extremal Quantile Regression
Thomas T. Yang
We revisit tail index regressions. For linear specifications, we find that the usual full rank condition can fail because conditioning on extreme outcomes causes regressors to dege…
Semiparametric Discrete Choice Models for Bundles
Fu Ouyang, Thomas Tao Yang
We propose two approaches to estimate semiparametric discrete choice models for bundles. Our first approach is a kernel-weighted rank estimator based on a matching-based identifica…
Semiparametric Discrete Choice Models for Bundles
Fu Ouyang, Thomas T. Yang
We propose two approaches to estimate semiparametric discrete choice models for bundles. Our first approach is a kernel-weighted rank estimator based on a matching-based identifica…
Revisiting Panel Data Discrete Choice Models with Lagged Dependent Variables
Christopher R. Dobronyi, Fu Ouyang, Thomas Tao Yang
This paper revisits the identification and estimation of a class of semiparametric (distribution-free) panel data binary choice models with lagged dependent variables, exogenous co…
A One-Covariate-at-a-Time Method for Nonparametric Additive Models
Liangjun Su, Thomas Tao Yang, Yonghui Zhang +1
This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similar…
Semiparametric Estimation of Dynamic Binary Choice Panel Data Models
Fu Ouyang, Thomas Tao Yang
We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has…
Quasi-Bayesian Inference for Production Frontiers
Xiaobin Liu, Thomas Tao Yang, Yichong Zhang
We propose a quasi-Bayesian method to conduct inference for the production frontier. This approach combines multiple first-stage extreme quantile estimates by the quasi-Bayesian me…