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数据不可合并情形下的双重差分法AI 解读

Difference-in-differences (DID) is commonly used to estimate treatment effects but is infeasible in settings where data are unpoolable due to privacy concerns or legal restrictions on data sharing, particularly across jurisdictions. In this study, we identify and relax the assumption of data poolability in DID estimation. We propose an innovative approach to estimate DID with unpoolable data (UN-DID) which can accommodate covariates, multiple groups, and staggered adoption.…

多个密度值时间序列的预测AI 解读

Density-valued time series are common in practice. However, since these functions are non-negative and must integrate to one, they do not form a linear vector space, which complicates the direct application of standard time-series methods. To address these constraints, we introduce two transformations, based on the centered log-ratio transformation and the cumulative distribution function, for modeling and forecasting balanced panel data represented by multiple densities.…

NIRVAR：网络信息约束向量自回归AI 解读

High-dimensional panels of time series often arise in finance and macroeconomics, where co-movements within groups of panel components occur. Extracting these groupings from the data provides a coarse-grained description of the complex system in question and can inform subsequent prediction tasks. We develop a novel methodology to model such a panel as a restricted vector autoregressive process, where the coefficient matrix is the weighted adjacency matrix of a stochastic…

不可忽略流失与补充抽样面板模型中的比例迭代估计与推断AI 解读

In panel data subject to nonignorable attrition, auxiliary (refreshment) sampling may restore full identification under weak assumptions on the attrition process. Despite their generality, these identification strategies have seen limited empirical use, largely because the implied estimation procedure requires solving a functional minimization problem for the target density. When the distributions of the observed data are parametric, we show that this problem can be solved…

广义线性模型中多层模型与固定效应方法的比较AI 解读

We extend prior work comparing linear multilevel models (MLM) and fixed effect (FE) models to the generalized linear model (GLM) setting, where the coefficient on a treatment variable is of primary interest. This leads to three insights. (i) First, as in the linear setting, MLM can be thought of as a regularized form of FE (RegFE). This explains why group-level confounding can greatly bias MLM's treatment coefficient estimates. However, unlike the linear setting, there is…

高维聚类数据中的估计目标偏移与合成近似AI 解读

In high-dimensional clustered data, covariates whose distributions differ across clusters can act as proxies for unobserved cluster effects. We show that the marginal-model LASSO implicitly uses sparse combinations of such covariates to shift the estimation target from structural coefficients to a contaminated vector, which inflates false selections. We therefore propose the Synthetic Heterogeneous-Effects LASSO (SHEL), which augments the regression design with separately…

多变量纵向数据的向量藤 Copula 模型AI 解读

Multivariate longitudinal data may exhibit non-Gaussian margins, nonlinear dynamics, and response vectors with composition that varies across waves. To account for these features, we introduce a vector drawable vine (VD-vine) copula that extends conventional drawable vine copulas from scalar to vector-valued nodes. Here, the response vector at each wave forms a multivariate marginal, and serial dependence is captured through a sequence of linking vector copulas. We establish…

超越行动者—伴侣关联：纵向双人数据因果溢出效应的目标试验框架AI 解读

Actor-partner interdependence models (APIMs) describe how one member's exposure is associated with that member's and the partner's outcomes, but actor and partner coefficients are causal only under additional design and identification assumptions. We formulate APIM-style questions as dyadic target trials under within-dyad interference and make three links between the associational and causal formulations explicit. First, under joint exchangeability, positivity, and a…

条件线性的矩阵正态状态空间模型AI 解读

We develop a class of linear state space models for matrix-valued time series data where the state is a latent matrix normal process. We derive matrix versions of the Kalman filter, log-likelihood, and smoother enabling estimation of the latent state matrix as well as the model's parameters. To conduct Bayesian inference, we provide algorithms that draw from the joint posterior distribution of the latent state matrices conditional on the observed data and parameters. We…

伪增量检验：利用自然发生的干预衡量广告增量效应AI 解读

We develop a method for measuring the incremental effect of advertising when randomized exper- iments are unavailable. Firms generate abrupt interventions in their own marketing as a byproduct of operations: budgets are cut, channels launch, programs pause. We propose a two-stage proce- dure that treats these events as quasi-experiments. The first stage discovers and dates interventions by exact Bayesian run-length inference on marketing activity series. The second stage…

动态选择下的因果推断：时变协变量与潜在异质性AI 解读

I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible interactions between time-varying covariates and latent heterogeneity. I use pretreatment outcome histories to find individuals with similar time-invariant latent…

不依赖收敛速度设定的成对数据回归 Wald 推断AI 解读

This paper develops Wald inference for least-squares estimation of linear regression models on dyadic data, accommodating configurations where multiple observations share the same pair of units (e.g., directed flows, multilayer networks, and dyadic panels). We establish that the dyadic-robust Wald statistic is asymptotically $χ^2_q$ for an arbitrary nonrandom sequence of full-rank restrictions, under a single condition on the accumulation of dependence. Throughout, no…

偏态结局纵向临床研究中的灵活治疗效应推断框架AI 解读

Longitudinal continuous outcomes in clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) under normality assumptions. However, many clinical outcomes are skewed, making mean-based treatment effects difficult to interpret and potentially reducing statistical efficiency. The Box--Cox MMRM (BCMMRM) approach accommodates skewness by enabling inference on model-based median differences via inverse transformation. However, BCMMRM typically assumes…

连续处理双重差分中的水平与响应两种边际AI 解读

This paper studies difference-in-differences with staggered adoption and a continuous, time-invariant dose. Each cohort-time comparison contains two margins. The level margin is the average treatment effect at realized doses. Under level parallel trends it equals the level contrast between the treated cohort and not-yet-treated controls. The response margin is the within-cohort slope of the outcome change on dose. It uses no controls, and its causal interpretation requires a…

广义协方差与相关系数AI 解读

The covariance of two random variables measures the average joint deviations from their respective means. We generalise this well-known measure by replacing the means with other statistical functionals such as quantiles, expectiles, or thresholds. Deviations from these functionals are defined via generalised errors, typically induced through identification or moment functions. As a normalised measure of dependence, a generalised correlation is constructed. Replacing the…

高维非平稳时间序列中多个同时变点的检测AI 解读

This paper studies the detection of multiple simultaneous (systematic) change points for high-dimensional nonstantionary economic and financial time series data. The analytic framework used is based on the standard and adaptive fused group lasso method, where the mixed L_{2,1} penalty is either uniform or re-weighted by data-dependent weights. This paper shows that, under appropriate conditions, this approach is L_2 consistent and, by adopting the data-dependent weights,…

合成最近邻：非随机缺失数据下的合成控制与矩阵补全AI 解读

We develop a causal framework for matrix completion under missing not at random (MNAR) data. Drawing on synthetic controls from the econometric panel data literature, our approach relaxes two assumptions common in MNAR matrix completion: positivity and independence of observation indicators. Unlike traditional panel data models, which often require prescribed block-sparse geometries, our framework accommodates flexible, heterogeneous observation patterns through…

合成增量效应：将合成控制推广至动态处理效应AI 解读

We propose a generalization of the synthetic control methods to the setting with dynamic treatment effects, in which each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a latent, endogenously time-varying confounding state. Under a low-rank latent factor model assumption, which admits linear time-varying and time-invariant dynamic triangular systems as special cases, we develop an identification strategy for any unit-specific…

不可转移效用双边网络形成模型的矩条件AI 解读

This paper investigates the construction of moment restrictions in dyadic network formation models with unobserved individual heterogeneity under nontransferable utility. Using observed links and covariates from five-node pentads, we construct moment restrictions that do not depend on individual fixed effects. For a broad class of covariate specifications, the construction is minimal in the sense that it uses the least possible number of nodes and dyads. Based on these…

利用补充样本检验参数模型中的可观测变量选择假设AI 解读

In panels with sample selection (that may occur due to attrition, nonresponse, etc.), the assumption of selection on observables (missing at random, MAR) is commonly imposed despite often being implausible. However, this assumption becomes testable when a refreshment sample is available. We develop a statistical test of MAR based on a distance between two estimated distributions: one obtained using the standard inverse probability weighting (IPW) that is valid under MAR and…

线性分位数面板模型的识别AI 解读

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

具有潜在转移异质性的离散结果事件研究设计AI 解读

We develop an identification strategy for average treatment effects on the treated (ATT) in panel data with discrete outcomes. For such outcomes, the parallel trends assumption underlying difference-in-differences (DiD) fails in three distinct ways: mean reversion generates divergent trends when groups differ at baseline, counterfactual probabilities can leave the unit interval, and no single trend is well defined for multi-category outcomes. We replace parallel trends with…

平均处理效应的局部化：合成控制中的投影方法AI 解读

Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this paper, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear…

交错采用设计下多层面板线性泛函的估计AI 解读

We study the estimation of bilinear forms from noisy, partially observed multilayer data. The signal follows a Tucker2 model, with shared unit and time factors across tensor layers and slice-specific cores. The missingness pattern is structured and motivated by staggered adoption designs, which are common in causal inference and related applications. We first analyze the four-block missingness pattern, the basic building block for general staggered adoption, and propose a…