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含不可观测个体特定阈值效应的面板阈值回归AI 解读

This paper provides a new approach to estimation and inference in multiple regime panel threshold regression with unobserved individual-specific threshold effects. Such effects are practically relevant and have features that distinguish them from traditional linear panel data models. It is shown that within-regime demeaning in the static model or within-regime first-differencing in the dynamic model both fail to deliver consistent threshold estimators. Instead, correlated…

函数型数据变化率的在线学习AI 解读

With advances in modern data collection technologies, functional data are increasingly received in a streaming manner. Although numerous methods have been developed to model such data, most focus on estimating the mean and covariance functions, while giving limited attention to their derivatives. In this paper, we introduce an online method for estimating the change rates of the mean and covariance functions, enabling real-time updates with high statistical efficiency. The…

内生门槛回归的新控制函数方法AI 解读

This paper studies control function (CF) approaches in endogenous threshold regression where the threshold variable is allowed to be endogenous. We first use a simple example to show that the structural threshold regression (STR) estimator of the threshold point in Kourtellos, Stengos and Tan (2016, Econometric Theory 32, 827–860) is inconsistent unless the endogeneity level of the threshold variable is low compared to the threshold effect. We correct the CF in the STR…

具有门槛边界的门槛回归AI 解读

This article studies computation, estimation, inference, and testing for linearity in threshold regression with a threshold boundary. We first put forward a new algorithm to ease the computation of the threshold boundary, and develop the asymptotics for the least squares estimator in both the fixed-threshold-effect framework and the small-threshold-effect framework. We also show that the inverting-likelihood-ratio method is not suitable to construct confidence sets for the…

存在内生性的门槛回归AI 解读

This paper studies estimation in threshold regression with endogeneity in the regressors and thresholding variable. Three key results differ from those in regular models. First, both the threshold point and the threshold effect parameters are shown to be identified without the need for instrumentation. Second, in partially linear threshold models, both parametric and nonparametric components rely on the same data, which prima facie suggests identification failure. But, as…

断点未知的断点回归设计：检验与估计AI 解读

The regression discontinuity design has become a common framework among applied economists for measuring treatment effects. A key restriction of the existing literature is the assumption that the discontinuity point is known, which does not always hold in practice. This paper extends the applicability of the regression discontinuity design by allowing for an unknown discontinuity point. First, we construct a unified test statistic to check whether there are selection or…

阈值回归中阈值点的自适应估计AI 解读

This paper studies semiparametric efficient estimation of the threshold point in threshold regression. The classical literature of semiparametric efficient estimation rests on the fact that the maximum likelihood estimator is efficient in any parametric submodel for a large class of loss functions. However, in threshold regression, the maximum likelihood estimator is not efficient, while the Bayes estimators are efficient and different loss functions induce different…

门槛回归中的 BootstrapAI 解读

This paper develops a general procedure to check the bootstrap validity in M -estimation. We apply the procedure in discontinuous threshold regression to show the inconsistency of the nonparametric bootstrap for inference on the threshold point. Especially, the conditional weak limit of the nonparametric bootstrap is shown not to exist. By comparing with two other boundaries in the literature, we show the fact that the threshold point is a boundary of the covariate that…

门槛回归中的似然估计与推断AI 解读

This paper studies likelihood-based estimation and inference in parametric discontinuous threshold regression models with i.i.d. data. The setup allows heteroskedasticity and threshold effects in both mean and variance. By interpreting the threshold point as a “middle” boundary of the threshold variable, we find that the Bayes estimator is asymptotically efficient among all estimators in the locally asymptotically minimax sense. In particular, the Bayes estimator of the…