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主分层可忽略性下因果效应的多重稳健估计AI 解读

Abstract Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stratification characterizes such a mechanism by targeting subgroup causal effects within principal strata, which are defined by the joint potential values of an intermediate variable. Due to the fundamental problem of causal inference, principal strata are inherently latent, rendering it…

含未观测异质性的大规模面板选择模型：贝叶斯与极大似然分析AI 解读

This paper considers the estimation and inference procedures for the case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed and the model is capable of capturing high-dimensional cross-section dependence. The proposed model also allows for heterogeneous regression coefficients. New Bayesian and non-Bayesian approaches are introduced to estimate the model parameters. We investigate the asymptotic behaviors of…

具有有限混合结构的计数 Roy 模型AI 解读

Summary This paper develops the Finite Mixture Roy model for count variables and uses this semiparametric model to analyze the effect of supplemental Medigap private insurance on the demand for prescription drugs for the U.S. elderly unemployed Medicare population. The model is an extension of the Count Roy model, which produces unrealistic treatment effects when observed count patterns are consistent with finite mixtures. To estimate the numbers of components in the…

含双向固定效应的指数回归工具变量估计及引力方程应用AI 解读

Summary This paper introduces instrumental‐variable estimators for exponential‐regression models that feature two‐way fixed effects. These techniques allow us to develop a theory‐consistent approach to the estimation of cross‐sectional gravity equations that can accommodate the endogeneity of policy variables. We apply this approach to a dataset in which the policy decision of interest is the engagement in a free‐trade agreement. We explore ways to exploit the transitivity…

具有超越对数生产技术的动态潜在因子技能形成模型识别AI 解读

Summary In this paper, we highlight an important property of the translog production function for the identification of treatment effects in a model of latent skill formation. We show that when using a translog specification of the skill technology, properly anchored treatment effect estimates are invariant to any location and scale normalizations of the underlying measures. By contrast, when researchers assume a CES production function and impose standard location and scale…

通过倾向得分实现协变量分布平衡AI 解读

Summary This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed procedure attempts to estimate a propensity score model by making the underlying covariate distribution of different treatment groups as close to each other as possible. Our estimators are data‐driven and can be used to estimate different treatment effect parameters under different…

用随机网络 SIR 模型匹配新冠疫情理论与证据AI 解读

Summary This paper develops an individual‐based stochastic network SIR model for the empirical analysis of the Covid‐19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under‐reporting of…

结合高维节点变量的网络联合潜在空间模型AI 解读

Summary Network latent space models assume that each node is associated with an unobserved latent position in a Euclidean , and such latent variables determine the probability of two nodes connecting with each other. In many applications, nodes in the network are often observed along with high-dimensional node variables, and these node variables provide important information for understanding the network structure. However, classical network latent space models have several…

异质系数、控制变量与多重处理效应识别AI 解读

Summary Multi-dimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the…

利用协变量的局部线性graphon估计AI 解读

Summary We consider local linear estimation of the graphon function, which determines probabilities of pairwise edges between nodes in an unlabelled network. Real-world networks are typically characterized by node heterogeneity, with different nodes exhibiting different degrees of interaction. Existing approaches to graphon estimation are limited to local constant approximations, and are not designed to estimate heterogeneity across the full network. In this paper, we show…

具有共享结构的多层网络潜在空间模型AI 解读

Summary Latent space models are frequently used for modelling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph. However, they are not well developed for more complex network structures, which are becoming increasingly common in practice. In this article we propose a new latent space model for multiplex networks, i.e., multiple heterogeneous networks observed on a shared node set. Multiplex…

存在不依从时意向治疗Wilcoxon–Mann–Whitney检验的相对效率AI 解读

Summary A general framework is set up to study the asymptotic properties of the intent-to-treat Wilcoxon–Mann–Whitney test in randomized experiments with nonignorable noncompliance. Under location-shift alternatives, the Pitman efficiencies of the intent-to-treat Wilcoxon–Mann–Whitney and $t$ tests are derived. It is shown that the former is superior if the compliers are more likely to be found in high-density regions of the outcome distribution or, equivalently, if the…

析因实验中的回归因果推断：目标量、模型设定与基于设计的性质AI 解读

Summary Factorial designs are widely used because of their ability to accommodate multiple factors simultaneously. Factor-based regression with main effects and some interactions is the dominant strategy for downstream analysis, delivering point estimators and standard errors simultaneously via one least-squares fit. Justification of these convenient estimators from the design-based perspective requires quantifying their sampling properties under the assignment mechanism…

分位数趋势滤波的风险界AI 解读

Summary We study quantile trend filtering, a recently proposed method for nonparametric quantile regression, with the goal of generalizing existing risk bounds for the usual trend-filtering estimators that perform mean regression. We study both the penalized and the constrained versions, of order $r \geqslant 1$, of univariate quantile trend filtering. Our results show that both the constrained and the penalized versions of order $r \geqslant 1$ attain the minimax rate up to…

惩罚回归学习动态治疗方案的泛化误差界AI 解读

A dynamic treatment regime (DTR) is a sequence of decision rules, one per stage of intervention, that maps up-to-date patient information to a recommended treatment. Discovering an appropriate DTR for a given disease is a challenging issue especially when a large set of prognostic variables are observed. To address this problem, we propose penalized regression-based learning methods with l1 penalty to estimate the optimal DTR that would maximize the expected outcome if…

网络干扰下处理效应估计的随机图渐近理论AI 解读

The network interference model for treatment effect estimation places experimental units at the vertices of an undirected exposure graph, such that treatment assigned to one unit may affect the outcome of another unit if and only if these two units are connected by an edge. This model has recently gained popularity as means of incorporating interference effects into the Neyman–Rubin potential outcomes framework; and several authors have considered estimation of various…

变换群不变分布的极限定理AI 解读

A distributional symmetry is invariance of a distribution under a group of transformations. Exchangeability and stationarity are examples. We explain that a result of ergodic theory implies a law of large numbers for such invariant distributions: If the group satisfies suitable conditions, expectations can be estimated by averaging over subsets of transformations, and these estimators are strongly consistent. We show that, if a mixing condition holds, the averages also…

动态固定效应 logit 模型的变换与矩条件AI 解读

This study proposes transformations for dynamic fixed effects logit models. First, these transformations construct valid moment conditions for the case with neither explanatory variables nor time dummies. Using valid moment conditions, we can obtain the first-order condition of the conditional maximum likelihood estimator proposed by Chamberlain (1985). Next, we derive valid moment conditions based on the transformations for the cases with strictly exogenous continuous…

随机系数模型的非参数估计：弹性网方法AI 解读

This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox et al. (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing this link, we extend the estimator, transforming it into a special case of the nonnegative elastic net. The extension improves the estimator’s recovery of the true support and allows for more accurate…

含非参数内生效应的横截面互动模型之筛工具变量估计AI 解读

In this study, we consider cross-sectional interaction models including spatial autoregressive models and peer effects models as special cases. Our model allows the endogenous effect – the effect of others’ outcomes on one’s own outcome – to be nonlinear and nonparametric. For the model estimation, we propose a sieve instrumental variable estimator and establish both its consistency and asymptotic normality. Furthermore, we propose a nonparametric specification test for the…

二元响应的半参数模型平均预测AI 解读

Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a…

股东积极主义的价值创造AI 解读

We measure value creation by activist investors via structural estimation of a model of the choice between passive investment and activism. Our estimates imply that average returns following activist intent announcements consist of 74.8% expected value creation, or treatment, 13.4% stock picking, and 11.8% sample selection effects. Higher treatment values predict improvements in firm performance and lower proxy contest probabilities, whereas abnormal announcements returns do…

企业会对性别薪酬差距透明化作出反应吗？AI 解读

ABSTRACT We examine the effect of pay transparency on the gender pay gap and firm outcomes. Using a 2006 legislation change in Denmark that requires firms to provide gender‐disaggregated wage statistics, detailed employee‐employer administrative data, and difference‐in‐differences and difference‐in‐discontinuities designs, we find that the law reduces the gender pay gap, primarily by slowing wage growth for male employees. The gender pay gap declines by 2 percentage points,…

分位数双重自回归模型AI 解读

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation…