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大量协变量下最优政策效应的推断AI 解读

Understanding the impact of the most effective policies or treatments on a response variable of interest is desirable in many empirical works in economics, statistics and other disciplines. Due to the widespread winner’s curse phenomenon, conventional statistical inference assuming that the top policies are chosen independent of the random sample may lead to overly optimistic evaluations of the best policies. In recent years, given the increased availability of large…

Retire：高维稳健期望分位数回归AI 解读

High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data. The former is computationally challenging due to the non-smooth nature of the check loss, and the latter is sensitive to heavy-tailed error distributions. In this paper, we propose and study (penalized) robust expectile regression…

社会网络的混合社群归属估计AI 解读

In economics and social science, network data are regularly observed, and a thorough understanding of the network community structure facilitates the comprehension of economic patterns and activities. Consider an undirected network with n nodes and K communities. We model the network using the Degree-Corrected Mixed-Membership (DCMM) model, where for each node i=1,2,…,n, there exists a membership vector πi=(πi(1),πi(2),…,πi(K))′, where πi(k) is the weight that node i puts on…

具有共同结构的面板多折点分位数回归模型AI 解读

Stimulated by the analysis of a data set on financial portfolio returns, we propose a multi-kink quantile regression (MKQR) model with latent homogeneous structure for panel data analysis. The proposed model accounts for both homogeneity and heterogeneity among individuals and parameters in panel data analysis. From statistical modeling point of view, it well balances the risk of misspecification and the model parsimony. From practical point of view, it is able to reveal not…

随机前沿分析中无效率分布设定的检验AI 解读

Stochastic frontier analysis is regularly used in empirical studies to evaluate the productivity and efficiency of companies. A typical stochastic frontier model involves a parametric frontier subject to a composite error term consisting of an inefficiency and a random error. We develop new tests for specification of distribution of the inefficiency. We focus on simultaneous relaxation of two common assumptions: (1) parametric frontier which may lead to false conclusions…

量化繁文缛节对投资的影响：企业调查数据方法AI 解读

An important strand of research in macro-finance investigates which factors impede enterprise investment, and what is their aggregate economic cost. In this paper, we make two contributions to this literature. The first contribution is methodological: we introduce a novel framework to calibrate macroeconomic models with firm-level distortions using enterprise survey micro-data. The core of our innovation is to explicitly model the firms' decisions to report in the survey the…

学习企业的消费风险暴露AI 解读

We structurally estimate an investment-based asset pricing model, in which firms' exposure to macroeconomic risk is unknown. Bayesian beliefs about this parameter are updated from firms' and industry peers' comovement between their productivity and consumption growth. The model implies that discount rates rise endogenously with the perceived risk exposure of firms, thereby depressing investment and valuation ratios. We test these predictions in the data and find strong…

通过幂变换序贯估计结构方程AI 解读

This study provides an econometric methodology to test a linear structural relationship among economic variables. We propose the so-called distance-difference (DD) test and show that it has omnibus power against arbitrary nonlinear structural relationships. If the DD-test rejects the linear model hypothesis, a sequential testing procedure assisted by the DD-test can consistently estimate the degree of a polynomial function that arbitrarily approximates the nonlinear…

基于含噪观测的分布推断AI 解读

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical…

有序响应模型的简便半参数估计AI 解读

We propose two simple semiparametric estimation methods for ordered response models with an unknown error distribution. The proposed methods do not require users to choose any tuning parameters, and they automatically incorporate the monotonicity restriction of the unknown distribution function. Fixing finite-dimensional parameters in the model, we construct nonparametric maximum likelihood estimates for the error distribution based on the related binary choice data or the…

学业困难学生能否受益于持续获得学生贷款？来自大学及毕业后的证据AI 解读

Abstract We estimate the effects of student loan access on educational attainment and labor market returns in New Zealand. We exploit the introduction of a national policy mandating a 50% pass rate for student loan renewals using a regression discontinuity design. Retaining loan access increases reenrollment for students around the threshold, and a majority eventually graduate with a bachelor's degree within seven years. We find that retaining student loan access leads to…

因果推断中匹配与加权调整手册AI 解读

For observational studies, causal inference often relies on adjustment methods for the adjustment of confounding effects. The research of adjustment methods has a long history, and, in recent years...

因果推断基础：使用RAI 解读

Fundamentals of Causal Inference: With R, by Babette Brumback provides an excellent introduction to causal inference methods and their implementations using R. The book is well-written, with many t...

刻画精神障碍决策行为的半参数逆强化学习方法AI 解读

Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to…

公平的政策定向配置AI 解读

One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities across sensitive attributes such as age, gender, or race. This article addresses the question of the design of fair and efficient treatment allocation rules. We adopt the nonmaleficence perspective of “first do no harm”: we select the fairest allocation within the Pareto frontier. We cast the optimization into a…

连续暴露下基于广义倾向得分的匹配AI 解读

In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still underdeveloped. We propose an innovative matching approach to estimate an average causal exposure-response function under the setting of continuous exposures that relies on the generalized propensity score (GPS). Our approach maintains the following attractive features of matching: (a) clear…

社会网络数据的因果推断AI 解读

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information…

通过 pT-Learning 估计无限期最优动态治疗方案AI 解读

Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals’ health statuses and deliver just-in-time personalized interventions. However, the practical use of mHealth technology raises unique challenges to existing methodologies on learning an optimal dynamic treatment regime. Many mHealth applications involve decision-making with large numbers of intervention options and under an infinite time horizon setting where the number of…

调整还是不调整？协变量缺失时随机实验平均处理效应的估计AI 解读

Complete randomization allows for consistent estimation of the average treatment effect based on the difference in means of the outcomes without strong modeling assumptions on the outcome-generating process. Appropriate use of the pretreatment covariates can further improve the estimation efficiency. However, missingness in covariates is common in experiments and raises an important question: should we adjust for covariates subject to missingness, and if so, how? The…

存在混杂的马尔可夫决策过程中的离线策略价值置信区间估计AI 解读

This article is concerned with constructing a confidence interval for a target policy’s value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this article, we show that with some auxiliary variables that mediate the…

个体处理效应的共形敏感性分析AI 解读

Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To assess the robustness of individual-level causal conclusion with unconfoundedness, this paper proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we…

用于时变网络的贝叶斯马尔可夫切换张量回归AI 解读

Modeling time series of multilayer network data is challenging due to the peculiar characteristics of real-world networks, such as sparsity and abrupt structural changes. Moreover, the impact of external factors on the network edges is highly heterogeneous due to edge- and time-specific effects. Capturing all these features results in a very high-dimensional inference problem. A novel tensor-on-tensor regression model is proposed, which integrates zero-inflated logistic…

随机区组析因实验的随机化联合中心极限定理与高效协变量调整AI 解读

Randomized block factorial experiments are widely used in industrial engineering, clinical trials, and social science. Researchers often use a linear model and analysis of covariance to analyze experimental results; however, limited studies have addressed the validity and robustness of the resulting inferences because assumptions for a linear model might not be justified by randomization in randomized block factorial experiments. In this article, we establish a new finite…

统一的协变量自适应随机化程序族及其性质AI 解读

In clinical trials and other comparative studies, covariate balance is crucial for credible and efficient assessment of treatment effects. Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring. In the literature, most studies have focused on balancing of discrete covariates. Applications of CAR with continuous covariates remain rare, especially when the interest goes beyond balancing only the first…