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含内生性的删失部分线性分位数回归AI 解读

This paper considers the estimation of a censored partial linear quantile regression model with endogeneity. Our method extends those for partial linear quantile regression (Lee, 2003, Qu et al. 2024), nonparametric quantile regression (Qu and Yoon, 2015, Belloni et al., 2019), and censored linear quantile regression with endogeneity (Chen, 2018). Our estimation method involves a tractable sequential-multiple-step procedure. We establish the uniform consistency, the uniform…

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…

含时变回归量与固定效应的持续期模型半参数估计AI 解读

In this paper, we consider estimation of an accelerated failure time model with time-varying regressors and fixed effects for duration data. We propose computationally simple profiled estimators for both fixed and random censoring cases. Under regularity conditions, we establish consistency and asymptotic normality of the estimators. Simulation studies demonstrate that our estimators perform well in finite samples. Finally, we use data from the First Malaysian Family Life…

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…

群组层面处理的分位数回归AI 解读

To study distributional effects of group level treatments, Chetverikov et al. (2016) proposed a grouped instrumental variables quantile regression estimator, a quantile extension of the Hausman and Taylor’s 1981 instrumental variables estimator for panel data. However, their approach only allows for heterogenous distributional effects of group-level treatments that correspond to individual-level unobserved characteristics, but not group-level unobserved characteristics. In…

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…

具有灵活加性结构与未知连接函数的广义加性模型：非参数识别与估计AI 解读

This paper proposes a nonparametric approach to identify and estimate the generalized additive model with a flexible additive structure and with possibly discrete variables when the link function is unknown. Our approach allows for a flexible additive structure which provides applied researchers the flexibility to specify their model according to economic theory or practical experience. Motivated by the concerns from empirical research, our method also allows for multiple…

无单调性条件下弱可分模型的内生性处理AI 解读

We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general…

含时变解释变量的持续时间模型：删失分位数回归两步估计AI 解读

Common duration models are characterized by strong homogeneity and thus are highly restrictive in allowing for how regressors affect the conditional duration distribution. In particular, the implied sign and relative marginal quantile effects remain the same over the entire range of the conditional duration distribution, which rules out general heterogeneous effects in duration data. Quantile regression, which offers a flexible and unified framework that allows for general…

同时存在删失与样本选择的分位数回归AI 解读

Arellano and Bonhomme (2017) considered nonparametric identification and semiparametric estimation of a quantile selection model, and Arellano and Bonhomme (2017s) extended the estimation approach to the case with censoring. However, there are some major drawbacks associated with the approach in Arellano and Bonhomme (2017s). In this paper we consider nonparametric and semiparametric identification of the quantile selection model with censoring, and we further propose a…

具有固定效应的广义变换模型的非参数估计AI 解读

This paper considers a generalized panel data transformation model with fixed effects where the structural function is assumed to be additive. In our model, no parametric assumptions are imposed on the transformation function, the structural function, or the distribution of the idiosyncratic error term. The model is widely applicable and includes many popular panel data models as special cases. We propose a kernel-based nonparametric estimator for the structural function.…

Summary Early studies of the generalized transformation panel data model resorted to the identical marginal distribution of the error term over time. This stationarity condition is restrictive for many applications, especially as the number of time periods increases. This paper considers nonstationary censored generalized transformation panel data models where the idiosyncratic error has an unknown nonseparable form and admits a flexible relationship between the observable…

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…

含内生性的删失回归模型的半参数估计AI 解读

Censoring and endogeneity are common in empirical applications. However, the existing semiparametric estimation methods for the censored regression model with endogeneity under an independence restriction are associated with some drawbacks. In this paper we propose a new semiparametric estimator that overcomes these drawbacks. We derive conditional quantile moment conditions for all the conditional quantiles and propose a moment-based estimator. In particular, we construct…

含时变解释变量的持续期模型分位数回归AI 解读

Since the seminal work of Koenker and Bassett (1978), quantile regression has become a widely used tool in duration analysis. The existing literature, however, has focused on time-invariant regressors, even though time-varying regressors are common in practice. In this paper, we introduce a quantile regression framework with time-varying regressors and develop quantile regression estimators. Our estimators are motivated by Manski’s (1975, 1985) maximum score estimator and…

动态二元选择面板模型中的排除限制：评《无严格外生回归变量的半参数二元选择面板模型》AI 解读

In this note we revisit the use of exclusion restrictions in the semiparametric binary choice panel data model introduced in Honore and Lewbel (2002). We show that in a dynamic panel data setting (where one of the pre-determined explanatory variables is the lagged dependent variable), the exclusion restriction in Honore and Lewbel (2002) implicitly re- quires serial independence condition on an observed regressor, that if violated in the data will result in their procedure…

删失分位数回归模型的序贯估计AI 解读

In this paper we propose sequential censored quantile regression (SCQR) and sequential instrumental variables censored quantile regression estimators (SIVCQR). We effectively transform the difficult censored quantile regression and censored instrumental variables quantile regression problems into more standard QR and IVQR procedures, consequently, our approaches make the quantile regression techniques for censored data easily accessible to applied researchers. Simulation…

不要求单调性或可分性的面板模型半参数估计AI 解读

Nonseparable panel data models with fixed effects have received a great deal of attention in the literature. Monotonicity is a common assumption in these settings, which may be violated in practice. Monotonicity-based estimators are inconsistent and the associated inference misleading under misspecification. In this paper, we propose some semiparametric estimators without imposing the monotonicity restriction. Under regularity conditions, our estimators are consistent and…

异方差截断回归模型的非参数识别与估计AI 解读

We consider nonparametric identification and estimation of truncated regression models with unknown conditional heteroskedasticity. The existing methods (e.g., Chen (2010, Review of Economic Studies 77, 127–153)) that ignore heteroskedasticity often result in inconsistent estimators of regression functions. In this paper, we show that both the regression and heteroskedasticity functions are identified in a location-scale setting. Based on our constructive identification…

对称性限制下样本选择模型的非参数识别与估计AI 解读

Under a conditional mean restriction Das et al. (2003) considered nonparametric estimation of sample selection models. However, their method can only identify the outcome regression function up to a constant. In this paper we strengthen the conditional mean restriction to a symmetry restriction under which selection biases due to selection on unobservables can be eliminated through proper matching of propensity scores; consequently we are able to identify and obtain…

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…

异方差二元响应模型中特殊回归变量的信息含量AI 解读

We quantify the informational content of special regressors in heteroskedastic binary response models with median-independent or conditionally symmetric errors. Based on Lewbel (1998), a special regressor is additively separable in the latent payoff and conditionally independent from the error term. We find that with median-independent errors a special regressor does not increase the identifying power by a criterion in Manski (1988) or lead to positive Fisher information for…

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…

删失Box–Cox回归模型的分布无关估计AI 解读

The Box–Cox regression model has been widely used in applied economics. However, there has been very limited discussion when data are censored. The focus has been on parametric estimation in the cross-sectional case, and there has been no discussion at all for the panel data model with fixed effects. This paper fills these important gaps by proposing distribution-free estimators for the Box–Cox model with censoring in both the cross-sectional and panel data settings. The…