每日简报

We construct moment functions that are Neyman-orthogonal to a chosen order in parametric moment condition models involving nuisance parameters. These moment functions reduce sensitivity to estimation error in the nuisance parameters and, as such, offer a unified and tractable route to higher-order debiasing in a wide range of econometric models. The number of additional nuisance parameters required by our construction, beyond those already present in the original moment…

通过专家损失整合实现时间序列预测的专家混合快速训练AI 解读

We propose a novel Mixture-of-Experts framework, referred to as Expert Loss Integration MoE (EliMoE), for time series forecasting. EliMoE addresses the optimization problem arising from small gating weights by incorporating expert-specific losses, which provide each expert with a direct learning signal independent of the gate-assigned weight. Specifically, the overall objective comprises the base forecasting loss and expert-specific losses, allowing individual expert…

任意数据增强下随机特征回归的泛化误差刻画AI 解读

This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, where the number of covariates grows proportionally to the number of samples. We provide a tight characterization of the test error, measured in mean squared error, in terms only of the population quantities of the true data, as well as first and second order statistics of the augmentation scheme. Our results are valid under…

集合滤波的数据一致性方法AI 解读

Ensemble filtering of chaotic, partially observed systems is often performed with ensembles far smaller than the state dimension resulting in empirical covariances that are low rank. Subsequently, stochastic observation perturbations can degrade both accuracy and probabilistic calibration. We develop a data-consistent perspective on ensemble filtering and introduce the Quantity-of-Interest Principal Component Analysis Ensemble Data Consistent Filter (QPCA-EnDCF), which is a…

扩展 Wasserstein-GAN 因果分布学习：无密度估计与极小极大最优性AI 解读

Distributional causal inference requires estimating not only average treatment effects but also interventional outcome distributions, including quantiles, tail risks, and policy-dependent uncertainty. As a method for distributional causal inference, generative adversarial network (GAN)-based counterfactual methods are flexible tools for this task. However, these methods have several limitations. First, the objectives of certain techniques do not coincide with the statistical…

尾部依赖时间序列异方差高分位回归的生成式同质性检验AI 解读

We consider a high quantile homogeneity test to determine whether a certain set of explanatory variables has homogeneous effects on different high quantiles of the response variable in the tail. To accommodate for situations under both the null and the alternative, the auxiliary process in this case may no longer be treated as stationary, and the problem requires a joint analysis of both homoscedastic and heteroskedastic high quantiles. For this, we develop a novel Bahadur…

线性过程尾部依赖的度量：理论与实证AI 解读

The quantitative analysis of financial time series often reveals two distinct features that standard Gaussian frameworks fail to capture: heavy-tailed marginal distributions and the phenomenon of extreme co-movements.While extreme value theory characterizes marginal behavior, Copulas provide a functional bridge to describe the dependence structure independently of the marginals. We are proposing a different way of looking at the joint extremes on the basis of a dependence…

事件时间结局因果推断的样本量与功效计算AI 解读

This paper develops power and sample size formulas for causal inference with time-to-event outcomes. The target estimand is the marginal hazard ratio: the coefficient of a marginal structural Cox proportional hazard model with treatment as the only predictor. We extend the robust sandwich variance theory and derive the analytical form of the asymptotic variance for the inverse probability weighted partial likelihood estimator. Building on this, we derive a new analytical…

隐藏结果的近端因果推断AI 解读

Methods that rely on proxies, without imposing strong parametric structure, are increasingly used to deal with unobserved variables in causal inference. One influential line of this work reconstructs latent distributions used to identify the target functional by exploiting eigenvalue eigenvector structure. Within this framework, we first establish identification of the full data law in the presence of hidden outcomes, and then develop influence function based estimators for…

两类代理变量因果识别方法的比较AI 解读

Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable methods have emerged as a powerful solution. We contrast two major approaches in this framework: (1) bridge equation methods, which leverage solutions to integral equations to recover causal targets, and (2) array decomposition methods, which recover latent factors used to identify counterfactual quantities via eigendecomposition tasks.…

通过 Mallows 型模型平均实现广义最优参数迁移学习AI 解读

In many economic applications, multiple source datasets are available, but their effective combination is challenging due to heterogeneity across datasets. To address this problem, we study a parameter-transfer framework that shares only source-side estimates and propose a Mallows-type model averaging method for combining target and source models in the parametric setting. The weights are obtained from a Mallows-type criterion that is unbiased for the target prediction risk…

分布合成控制的渐近性质AI 解读

As an alternative to synthetic control, the distributional Synthetic Control (DSC) proposed by Gunsilius (2023) provides estimates for quantile treatment effect and thus enabling researchers to comprehensively understand the impact of interventions in causal inference. But the asymptotic properties of DSC have not been built. In this paper, we first establish the DSC estimator's asymptotic optimality in the essence that the treatment effect estimator given by DSC achieves…

人口干预中间接效应与干预变量效应的近端推断AI 解读

Unmeasured confounding, unethical exposure, and ill-defined interventions pose significant challenges to evaluating policy-relevant mediation estimands in medicine and public health. In observational studies involving harmful exposures, the population intervention indirect effect (PIIE) is often more salient than the natural indirect effect, as the latter relies on hypothetical interventions that may be ethically or practically unfeasible. While the PIIE can be identified…

用余差预测线性分数稳定运动：非高斯粗糙波动率应用AI 解读

The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering $α$-stable increments. We propose a method to forecast future increments of the LFSM from past discrete-time observations, using the conditional expectation when $α>1$ or a semimetric projection otherwise. It relies on the codifference, which describes the serial dependence of the process, instead of the covariance. Indeed, covariance is commonly used for predicting an fBm…

分布偏移与重叠衰减下的一般目标半参数半监督学习AI 解读

In modern scientific applications, large volumes of covariate data are readily available, while outcome labels are costly, sparse, and often subject to distribution shift. This asymmetry has spurred interest in semi-supervised (SS) learning, but most existing approaches rely on strong assumptions -- such as missing completely at random (MCAR) labeling or strict positivity -- that put substantial limitations on their practical usefulness. In this work, we introduce a general…

平行趋势违反时双重差分前匹配的偏差—方差权衡AI 解读

Quasi-experimental causal inference methods have become central in empirical operations management for guiding managerial decisions. Among these, empiricists utilize the Difference-in-Differences (DiD) estimator, which relies on the parallel trends assumption. To improve its plausibility, researchers often match treated and control units before applying DiD, with the intuition that matched groups are more likely to evolve similarly absent treatment. Existing work that…

后验漂移下的过度参数化模型AI 解读

This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We document the loss in performance when the loadings of the data generating process change between the training and testing samples. This matters crucially in settings in which regime changes are likely to occur, for instance, in financial markets. Applied to equity premium forecasting, our results underline the sensitivity of a market…

乘法准工具变量模型AI 解读

We introduce the Multiplicative Quasi-Instrumental Variable (MQIV) model, a framework for causal inference with unmeasured confounding that leverages an instrument that may be imperfectly exogenous. We allow the candidate quasi-instrument to have a direct effect on the outcome not mediated by the treatment, thus violating the standard IV exclusion restriction. We establish nonparametric identification of the population average treatment effect on the treated (ATT) under a…

作为协整系统的全球碳预算AI 解读

The Global Carbon Budget, maintained by the Global Carbon Project, summarizes Earth's global carbon cycle through four annual time series beginning in 1959: atmospheric CO$_2$ concentrations, anthropogenic CO$_2$ emissions, and CO$_2$ uptake by land and by ocean. We analyze these four time series as a multivariate (cointegrated) system. Statistical tests show that the four time series are cointegrated with rank three and identify anthropogenic CO$_2$ emissions as the single…

乘法工具变量模型AI 解读

The instrumental variable (IV) design is a common approach to address hidden confounding bias. For validity, an IV must impact the outcome only through its association with the treatment. In addition, IV identification has required a homogeneity condition such as monotonicity or no unmeasured common effect modifier between the additive effect of the treatment on the outcome, and that of the IV on the treatment. In this work, we introduce the Multiplicative Instrumental…