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利用文本数据预测欧洲 GDPAI 解读

Summary We evaluate the informational content of news‐based sentiment indicators for forecasting gross domestic product (GDP) and other macroeconomic variables of the five major European economies. Our dataset includes over 27 million articles for 26 major newspapers in five different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other…

内生删失持续期模型的部分识别与推断AI 解读

Abstract This paper studies identification and inference in transformation models with endogenous censoring. Many kinds of duration models, such as the accelerated failure time model, proportional hazard model, and mixed proportional hazard model, can be viewed as transformation models. We allow the censoring of a duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. We impose no parametric restrictions on either the…

服从者参数的双重稳健性与服从者特征的半参数检验AI 解读

Summary We propose a semi-parametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments. To justify the test, we characterise the doubly robust…

参数位于参数空间边界时的惩罚准似然估计与模型选择AI 解读

Summary We consider here penalized likelihood-based estimation and model selection applied to econometric time series models, which allow for nonnegativity (boundary) constraints on some or all of the parameters. We establish that joint model selection and estimation result in standard asymptotic Gaussian distributed estimators. The results contrast with nonpenalized estimation, which, as is well-known, leads to nonstandard asymptotic distributions that depend on the unknown…

随机右删失下的工具变量分位数回归AI 解读

Summary This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile…

含干扰泛函的复杂调查数据增广两步估计方程AI 解读

Summary Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves, and quantile shares are among the commonly encountered examples. The nuisance functionals are usually handled by a plug-in nonparametric estimator and the main inferential procedure can be carried out through a two-step generalized empirical likelihood method. Unfortunately, the resulting inference…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：政策评价局限AI 解读

We thank Sävje (2024) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Sävje recommended misspecified exposure effects as a way to avoid strong assumptions about interference when analysing the results of an experiment. In this discussion, we highlight a key limitation of Sävje’s recommendation. Exposure effects are not generally useful for evaluating social policies without the strong assumptions…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：推断所需的干扰限制AI 解读

We congratulate the author on an insightful paper that provides a discerning critique of the use of exposure mappings in studies with interference. The main observation is that exposure mappings serve two different functions; they define the estimand and impose structure on potential outcomes by restricting interference. The first function commonly leads researchers to select simple exposure mappings that summarize treatments assigned to units ‘near’ the ego. However, due to…

离线强化学习中自然随机策略的有效评估AI 解读

Summary We study the efficient off-policy evaluation of natural stochastic policies, which are defined in terms of deviations from the unknown behaviour policy. This is a departure from the literature on off-policy evaluation that largely considers the evaluation of explicitly specified policies. Crucially, off-line reinforcement learning with natural stochastic policies can help alleviate issues of weak overlap, lead to policies that build upon current practice and improve…

利用去偏随机梯度下降进行在线推断AI 解读

Summary We propose a debiased stochastic gradient descent algorithm for online statistical inference with high-dimensional data. Our approach combines the debiasing technique developed in high-dimensional statistics with the stochastic gradient descent algorithm. It can be used to construct confidence intervals efficiently in an online fashion. Our proposed algorithm has several appealing aspects: as a one-pass algorithm, it reduces the time complexity; in addition, each…

连续标记特异的分位数回归模型AI 解读

Summary Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times. An example of the continuous mark variable is the genetic distance that measures dissimilarity between…

广义 Oaxaca–Blinder 估计量的无损校准AI 解读

Summary In randomized experiments, adjusting for observed features when estimating treatment effects has been proposed as a way to improve asymptotic efficiency. However, among parametric methods, only linear regression has been proven to form an estimate of the average treatment effect that is asymptotically no less efficient than the treated-minus-control difference in means regardless of the true data generating process. Randomized treatment assignment provides this…

针对原因特异绝对风险与生存曲线的一步目标最大似然估计AI 解读

Summary This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent…

利用干预实验数据贝叶斯学习网络结构AI 解读

Summary Directed acyclic graphs provide an effective framework for learning causal relationships among variables given multivariate observations. Under pure observational data, directed acyclic graphs encoding the same conditional independencies cannot be distinguished and are collected into Markov equivalence classes. In many contexts, however, observational measurements are supplemented by interventional data that improve directed acyclic graph identifiability and enhance…

基于最大均值差异的通用稳健回归AI 解读

Abstract Many modern datasets are collected automatically and are thus easily contaminated by outliers. This has led to a renewed interest in robust estimation, including new notions of robustness such as robustness to adversarial contamination of the data. However, most robust estimation methods are designed for a specific model. Notably, many methods were proposed recently to obtain robust estimators in linear models, or generalized linear models, and a few were developed…

分层随机实验与匹配观察研究中的个体处理效应分位数AI 解读

Summary Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on…

M估计量的刻画AI 解读

Summary We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M-estimation. This novel characterization result allows us to leverage existing results on loss functions known from the literature on forecast evaluation in estimation theory. We exemplify advantageous implications for the fields of robust, efficient, equivariant…

再随机化设计的检验功效与样本量计算AI 解读

Summary Power analyses are an important aspect of experimental design, because they help determine how experiments are implemented in practice. It is common to specify a desired level of power and compute the sample size necessary to obtain that power. Such calculations are well known for completely randomized experiments, but there can be many benefits to using other experimental designs. For example, it has recently been established that rerandomization, where subjects are…

无标签演化树的统计摘要AI 解读

Summary Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While…

面向有限总体的定制推断：条件有效性与跨分布迁移AI 解读

Summary Parameters of subpopulations can be more relevant than those of superpopulations. For example, a healthcare provider may be interested in the effect of a treatment plan for a specific subset of their patients; policymakers may be concerned with the impact of a policy in a particular state within a given population. In these cases, the focus is on a specific finite population, as opposed to an infinite superpopulation. Such a population can be characterized by fixing…

模型选择后兼具信息量与一致渐近有效性的混合置信区间AI 解读

Abstract I propose a new type of confidence interval for correct asymptotic inference after using data to select a model of interest without assuming any model is correctly specified. This hybrid confidence interval is constructed by combining techniques from the selective inference and post-selection inference literatures to yield a short confidence interval across a wide range of data realizations. I show that hybrid confidence intervals have correct asymptotic coverage,…

可扩展子抽样：计算、聚合与推断AI 解读

Abstract Subsampling has seen a resurgence in the big data era where the standard, full-resample size bootstrap can be infeasible to compute. Nevertheless, even choosing a single random subsample of size b can be computationally challenging with both b and the sample size n being very large. This paper shows how a set of appropriately chosen, nonrandom subsamples can be used to conduct effective, and computationally feasible, subsampling distribution estimation. Furthermore,…

暴露映射误设下的因果推断：分离定义与假设AI 解读

Summary Exposure mappings facilitate investigations of complex causal effects when units interact in experiments. Current methods require experimenters to use the same exposure mappings to define the effect of interest and to impose assumptions on the interference structure. However, the two roles rarely coincide in practice, and experimenters are forced to make the often questionable assumption that their exposures are correctly specified. This paper argues that the two…

用于评估自回归过程处理效应的动态合成控制法AI 解读

Abstract Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the…