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存在期限结构时的稳健双重差分分析AI 解读

For variables with a term structure, the standard difference-in-differences (DiD) model is predisposed toward misspecification, even under random assignment, because of heterogeneity over the maturity spectrum and imperfect matching between treated and control units. Estimated treatment effects that are false, biased, or hard to interpret become a concern. Neither unit fixed effects nor standard term-structure controls resolve the problem. Solutions that overcome imperfect…

污染资产再配置：环境……的影响（原题截断）AI 解读

This paper investigates how firms’ pollution incentives are influenced by their ability to divest polluted assets. My empirical setting is a major reform that exempts purchasers from liability for past contamination. Using a difference-in-differences framework, I find that the reform reduces toxic emissions, lowers bankruptcy risk, and increases firm value. Cross-sectional tests show that the decline in emissions is driven by firms with weaker financial health and fewer…

跳跃扩散模型的渐近理论AI 解读

This paper presents an asymptotic theory for recurrent jump diffusion models with well-defined scale functions. The class of such models is broad, including general nonstationary as well as stationary jump diffusions with state-dependent jump sizes and intensities. The asymptotics for recurrent jump diffusion models with scale functions are largely comparable to the asymptotics for the corresponding diffusion models without jumps. For stationary jump diffusions, our…

平稳持久数据下的时变参数回归AI 解读

We consider local level and local linear estimators for estimation and inference in time-varying parameter (TVP) regressions with general stationary covariates. The latter estimator also yields estimates for parameter derivatives that are utilized for the development of time invariance tests for the regression coefficients. Our theoretical framework is general enough to allow for a wide range of stationary regressors, including stationary long memory. We demonstrate that…

动态二元选择面板数据模型的半参数估计AI 解读

We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model under consideration has the same random utility framework as in Honoré and Kyriazidou (2000, Econometrica 68, 839–874). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model…

结合代理变量与叙事符号限制：重新考察技术冲击的影响AI 解读

ABSTRACT This paper proposes a novel integration of the proxy structural vector autoregression approach with narrative sign restrictions to identify multiple shocks with multiple instrumental variables. We show that this combination leads to informative inferences on shock effects through both analytical demonstrations and Monte Carlo simulations. Applying this method to identify anticipated and unanticipated technology shocks, we find that anticipated shocks account for…

有限个异质聚类下的置换推断AI 解读

Abstract I introduce a simple permutation procedure to test conventional (nonsharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when the treatment effect is identified by comparisons across clusters. The procedure asymptotically controls size by applying a level-adjusted permutation test to a suitable statistic. The adjusted permutation test is easy to implement in practice and performs well at…

删失事件时间数据中因果净获益的去偏学习AI 解读

Summary Cox regression is the default approach to evaluating the (relative) effect of two treatments on a survival endpoint. This standard framework has nonetheless been criticized for its canonical effect measure, the hazard ratio, having a subtle interpretation, thereby hindering policy-making. This in turn has prompted interest in other effects measures, such as the difference in restricted mean survival time, the net benefit and the win ratio, which have become…

有序结果中因果必要性概率的识别与界限AI 解读

Summary Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment, given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with…

非可识别高斯模型中有向无环图学习的整数规划AI 解读

Summary We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model…

期望的光滑函数之无偏蒙特卡洛估计：迈向自动化方法AI 解读

Abstract Given a smooth function $ f $, we develop a general approach to turn Monte Carlo samples with expectation $ m $ into an unbiased estimate of $ f(m) $. Specifically, we develop estimators that are based on randomly truncating the Taylor series expansion of $ f $ and estimating the coefficients of the truncated series. We derive their properties and propose a strategy to set their tuning parameters (which depend on $ m $) automatically, with a view to making the whole…

协变量自适应随机化临床试验中的一般协变量调整AI 解读

Abstract In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we…

高维重复事件的动态因子分析AI 解读

Summary Recurrent event time data arise in many studies, including in biomedicine, public health, marketing and social media analysis. High-dimensional recurrent event data involving many event types and observations have become prevalent with advances in information technology. This article proposes a semiparametric dynamic factor model for the dimension reduction of high-dimensional recurrent event data. The proposed model imposes a low-dimensional structure on the mean…

删失数据下纵向替代指标的稳健评估AI 解读

Abstract The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g. fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the…

超越总体变量重要性：个体变量重要性的概念、理论与应用AI 解读

Abstract In a non-parametric regression setting, we introduce a novel concept of ‘individual variable importance’, which assesses the relevance of certain covariates to an outcome variable among individuals with specific characteristics. This concept holds practical importance for both risk assessment and association identification. For example, it can represent (i) the usefulness of expensive biomarkers in risk prediction for individuals at a specified baseline risk, or…

随机游走 Metropolis 算法的可扩展耦合AI 解读

Abstract There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exploiting embarrassingly parallel computing capabilities. Motivated by these, we consider the design and analysis of couplings of the random walk Metropolis algorithm which scale well with the dimension of the target measure. Methodologically, we introduce a low-rank modification of the…

含噪标签的自适应保形分类AI 解读

Abstract This article develops a conformal prediction method for classification tasks that can adapt to random label contamination in the calibration sample, often leading to more informative prediction sets with stronger coverage guarantees compared to existing approaches. This is obtained through a precise characterization of the coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable…

高维稳健角度迁移学习AI 解读

Abstract Transfer learning improves target model performance by leveraging data from related source populations, especially when target data are scarce. This study addresses the challenge of training high-dimensional regression models with limited target data in the presence of heterogeneous source populations. We focus on a practical setting where only parameter estimates of pretrained source models are available, rather than individual-level source data. For a single…

稳健性、模型检验与层级模型AI 解读

Abstract Model checking is essential to evaluate the adequacy of statistical models and the validity of inferences drawn from them. Particularly, hierarchical models such as latent Gaussian models (LGMs) pose unique challenges as it is difficult to check assumptions on the latent parameters. Diagnostic statistics are often used to quantify the degree to which a model fit deviates from the observed data. We construct diagnostic statistics by (a) defining an alternative model…

自适应函数型主成分分析AI 解读

Abstract Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although smoothing parameters affect the subsequent inference, data-driven methods for selecting these parameters are not well-developed, frustrated by the difficulty of using all the information shared by curves while being computationally efficient. On the one hand, smoothing individual curves in…

因果中介分析：选择与渐近有效推断AI 解读

Abstract Researchers are often interested in learning not only the effect of treatments on outcomes, but also the mechanisms that transmit these effects. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In…

含干预的半机制流行病模型：频率学派推断AI 解读

Abstract The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this article, we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use…

渐近相关与渐近独立极端事件的二元分类器评估AI 解读

Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this article, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is…

Cox模型中通信高效的分布式估计与推断AI 解读

Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct…