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具有二元分位数选择的分位数回归半参数估计AI 解读

This article proposes a novel method for estimating quantile regression models that account for sample selection. Unlike the approach by Arellano and Bonhomme (2017, Econometrica 85(1), 1–28; hereafter referred to as AB17), which employs a parametric selection equation, our method utilizes a standard binary quantile regression model to handle the selection issue, thereby accommodating general heterogeneity in both the selection and outcome equations. We adopt a…

利用删失选择分位数回归估计英国工资不平等AI 解读

Arellano and Bonhomme (2017) proposed a quantile selection model to study the evolution of wage inequality in the UK, which specifies a binary selection equation and requires an exclusion restriction. In this paper we propose a quantile selection model with a more informative censored selection equation. Following Heckman (1974, 1979), Heckman and Sedlacek (1990), and Blundell et al. (2003), among others, the employment selection equation could be equivalently modeled by an…

删失选择下的分布回归AI 解读

Chernozhukov, Fernández-Val, and Luo (2023, CFL (2023) hereafter) considered a distribution regression model subject to sample selection with a binary selection mechanism. In this paper, we show how to identify and estimate a semi-parametric distribution regression model subject to a censored selection rule. With censored selection, we do not need to impose the usual outcome exclusion restriction or exclusion of the level of the latent selection variable from the selection…

广义异方差变换回归模型的 n-预测AI 解读

Chen (2010) and Khan (2001) consider quantile regression estimation subject to general transformation that permits general heteroscedasticity, but the resulting conditional quantile predictors converge at rates slower than the parametric rate. In this paper, we consider the estimation of a general transformation model subject to a multiplicative form of heteroscedasticity. Our estimators for the finite dimensional parameters, the transformation function and the resulting…

含未知相关随机效应的二元响应面板模型的√N一致估计AI 解读

In this article, we consider the estimation of a panel data binary response model with a weak restriction imposed on the individual specific effects. Our estimator is n-consistent and asymptotically normal under reasonable regularity conditions. Furthermore, we allow the error terms to be heteroscedastic over time. The proposed estimator has a closed form expression and thus is very easy to compute. Simulations and the empirical illustration demonstrate the usefulness of our…

基于局部多项式平滑的二元分位数回归AI 解读

Manski (1975, 1985) proposed the maximum score estimator for the binary choice model under a weak conditional median restriction that converges at the rate of n−1/3 and the standardized version has a nonstandard distribution. By imposing additional smoothness conditions, Horowitz (1992) proposed a smoothed maximum score estimator that often has large finite sample biases and is quite sensitive to the choice of smoothing parameter. In this paper we propose a novel framework…