论文检索

连续时间动态离散选择博弈的识别与估计AI 解读

This paper considers the theoretical, computational, and econometric properties of continuous‐time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a generalized version with heterogeneous move arrival rates. We reestablish conditions for existence of a Markov perfect…

扩大心理治疗可及性的因果效应AI 解读

Abstract This paper estimates the causal effect of scaling up access to psychotherapy. I study a 2008 reform of the Danish public health insurance, which introduced 60% coverage for psychotherapy by private practice psychologists for patients aged 18–37 diagnosed with depression or anxiety. Using administrative data from 1995–2019 and quasi-experimental methods, I show that psychotherapy coverage reduces psychiatric hospital contacts, physical health care use, and suicide…

样本选择下的分布回归与英国工资分解AI 解读

We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model, which accommodates much richer patterns of heterogeneity in the effect of covariates and selection process and allows for departures from Gaussian errors while maintaining the same level of tractability. The model applies to continuous, discrete, and mixed outcomes. We provide identification, estimation, and inference…

动态网络的双重异质性模型AI 解读

Analysis of networks that evolve dynamically requires the joint modelling of individual snapshots and time dynamics. This paper proposes a new flexible two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity to form ties with other nodes statically and the other to differentiate the tendency to retain existing ties over time. With n observed networks each having p…

网络干扰下基于平均场方法的因果效应估计AI 解读

We study causal effect estimation from observational data under interference. The interference pattern is captured by an observed network. We adopt the chain graph framework of (J. Amer. Statist. Assoc. 116 (2021) 833–844), which allows (i) interaction among the outcomes of distinct study units connected along the graph and (ii) long range interference, whereby the outcome of an unit may depend on the treatments assigned to distant units connected along the interference…

序贯实验中的反事实推断AI 解读

We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies that adapt over time. Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale—mean outcome under different treatments for each unit and each time—with minimal assumptions on the adaptive treatment policy. Without any structural assumptions on the…

分位数回归的渐近精确选择后推断AI 解读

In modern data analysis, it is common to select a model before performing statistical inference. Selective inference tools make adjustments for the model selection process in order to ensure reliable inference post selection. In this paper, we introduce an asymptotic pivot to infer about the effects of selected variables on conditional quantile functions. Utilizing estimators from smoothed quantile regression, our proposed pivot is easy to compute and yields…

通过协变量调整消除因果目标选择偏差的共同原因原则AI 解读

Average treatment effects (ATEs) may be subject to selection bias when they are estimated among only a nonrepresentative subset of the target population. Selection bias can sometimes be eliminated by conditioning on a “sufficient adjustment set” of covariates, even for some forms of missingness not at random (MNAR). Without requiring full specification of the causal structure, we consider sufficient adjustment sets to allow nonparametric identification of conditional ATEs in…

异质处理效应与异方差下的模型平均估计AI 解读

The primary focus of this article is to capture heterogeneous treatment effects measured by the conditional average treatment effect. A model averaging estimation scheme is proposed with multiple candidate linear regression models under heteroskedastic errors, and the properties of this scheme are explored analytically. First, it is shown that our proposal is asymptotically optimal in the sense of achieving the lowest possible squared error. Second, the convergence of the…

完备性是否必要？未识别线性模型中的估计AI 解读

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the nonsingularity of a high-dimensional matrix or operator and may fail for finite samples or even at the population level. Regularized estimators provide a…

可能包含大量协变量的断点回归设计AI 解读

This article examines high-dimensional covariates in regression discontinuity design (RDD) analysis. We introduce estimation and inference methods for the RDD models that incorporate covariate selection while maintaining stability across various numbers of covariates. The proposed methods combine a localization approach using kernel weights with $\ell _{1}$ -penalization to handle high-dimensional covariates. We provide both theoretical and numerical evidence demonstrating…

函数型工具变量回归及移民对本土工资影响的估计AI 解读

Functional linear regression has gained popularity as a statistical tool for studying the relationship between function-valued variables. However, in practice, it is hard to expect that the explanatory variables of interest are strictly exogenous, due to, for example, the presence of omitted variables and measurement error. This issue of endogeneity remains insufficiently explored, in spite of its empirical importance. To fill this gap, this article proposes new consistent…

单调样本流失下的有效估计AI 解读

Attrition is monotonic when agents leaving multi-period studies do not return. Under a general missing at random (MAR) assumption, we study efficiency in estimation of parameters defined by moment restrictions on the distributions of the counterfactuals that were unrealized due to monotonic attrition. We discuss novel issues related to overidentification, usability of sample units, and the information content of various MAR assumptions for estimation of such parameters. We…

通过边际处理效应进行福利分析AI 解读

We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing…

有序结果中内生变量效应的估计AI 解读

Abstract We examine the use of instrumental variable (IV) methods to measure the effect of a ceteris paribus change in an endogenous variable on an ordered outcome. Specifically, we use these methods to investigate the effect of neighborhood characteristics on subjective well-being (SWB) among participants in the Moving to Opportunity (MTO) housing voucher experiment. We find that the estimated positive effect of a decrease in neighborhood poverty on SWB is sensitive to the…

保序倾向得分匹配AI 解读

We propose a one-to-many matching estimator of the average treatment effect based on propensity scores estimated by isotonic regression. This approach is predicated on the assumption of monotonicity in the propensity score function, a condition that can be justified in many economic applications. We show that the nature of the isotonic estimator can help us to fix many problems of existing matching methods, including efficiency, choice of the number of matches, choice of…

数值型成对比较数据的最小二乘估计AI 解读

Abstract Least square estimators for graphical models for cardinal paired comparison data with and without covariates are rigorously analysed. Novel, graph-based, necessary, and sufficient conditions that guarantee strong consistency, asymptotic normality, and the exponential convergence of the estimated ranks are emphasized. A complete theory for models with covariates is laid out. In particular, conditions under which covariates can be safely omitted from the model are…

连续主层的半参数局部化主分层分析AI 解读

Abstract Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects (PCEs). Inspired by recent research, we resolve these challenges by…

协变量辅助的工具变量因果效应界限AI 解读

Abstract When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the marginal average treatment effect (ATE) from IVs relies on strong untestable structural assumptions. When one is unwilling to assert such structure, IVs can nonetheless be used to construct bounds on the ATE. Famously, Alexander Balke and Judea Pearl proved tight bounds on the ATE for a…

SymmPI：具有群对称性数据的预测推断AI 解读

Abstract Quantifying the uncertainty of predictions is a core problem in modern statistics. Methods for predictive inference have been developed under a variety of assumptions, often—for instance, in standard conformal prediction—relying on the invariance of the distribution of the data under special groups of transformations such as permutation groups. Moreover, many existing methods for predictive inference aim to predict unobserved outcomes in sequences of feature-outcome…

失业冲击、失业救济与家庭暴力AI 解读

Abstract We estimate impacts of male job loss, female job loss, and male unemployment benefits on domestic violence (DV) in Brazil. We merge individual-level employment and welfare registers with different measures of DV: judicial cases brought to criminal courts, the use of public shelters by victims, and mandatory DV notifications by health providers. Leveraging mass layoffs for identification, we first show that both male and female job loss, independently, lead to large,…

仅有一个处理簇时的统计推断AI 解读

Abstract I introduce a generic method for inference about a scalar parameter in research designs with a finite number of heterogeneous clusters where only a single cluster received treatment. This situation is commonplace in difference-in-differences estimation, but the test developed here applies more broadly. I show that the test controls size and has power under asymptotics where the number of observations within each cluster is large but the number of clusters is fixed.…

最大得分目标参数的有限样本推断AI 解读

Abstract We provide a finite sample inference method for the structural parameters of Manski's semiparametric binary response model under a conditional median restriction. This is achieved by exploiting distributional properties of observable outcomes conditional on the observed sequence of exogenous variables. Moment inequalities conditional on the size n sequence of exogenous covariates are constructed, and the proposed test statistic is a monotone function of violations…

加权平均分位数回归AI 解读

In this paper, we introduce the weighted-average quantile regression model. We argue that this model is of interest in many applied settings and develop an estimator for parameters of this model. We show that our estimator is T-consistent and asymptotically normal under weak conditions, where T is the sample size. We demonstrate the usefulness of our estimator in two empirical settings. First, we study the factor structures of the expected shortfalls of the industry…