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Summary It has long been established that if a panel dataset suffers from attrition, auxiliary (refreshment) sampling restores full identification under additional assumptions that still allow for nontrivial attrition mechanisms. Such identification results either rely on implausible assumptions about the attrition process or lead to theoretically and computationally challenging estimation procedures. We propose an alternative identifying assumption that, despite its…

分期实施政策的合成控制推断AI 解读

Summary Many policies are replicated by other policymakers at different times. We introduce a synthetic control methodology to study policies with staggered adoption. Our method estimates the dynamic average treatment effects on the treated using variation introduced by the staggered adoption of policies. Our method gives asymptotically unbiased estimators of many interesting quantities and delivers asymptotically valid inference. Applying the method to intergovernmental…

存在溢出效应时合成控制方法的识别与贝叶斯推断AI 解读

Summary The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption, ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends the standard SCM to account for spillovers and estimate both treatment and spillover effects. The…

用于中介效应检验的有限样本增广似然比检验AI 解读

Summary Testing for mediation suffers from low power near the origin. This can be addressed by augmenting the likelihood ratio critical region as discussed in previous work within an asymptotic framework. In this paper we consider exact distributions of test statistics and use this to derive optimal augmentation regions for arbitrary sample sizes. The resulting test is very simple and reliable, making it relevant for empirical research, even with a very small number of…

固定 T 异质性面板模型的双向均值组估计AI 解读

Summary We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. The model allows slope coefficients to be arbitrarily correlated with the regressors, accommodating flexible forms of heterogeneity. We propose a two-way mean group estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We apply our new methods to examine the…

高维 VAR(k) 协整检验的渐近理论AI 解读

Abstract The paper studies nonstationary high-dimensional vector autoregressions of order k, VAR(k). Additional deterministic terms such as trend or seasonality are allowed. The number of time periods, T, and the number of coordinates, N, are assumed to be large and of the same order. Under this regime the first-order asymptotics of the Johansen likelihood ratio (LR), Pillai-Bartlett, and Hotelling-Lawley tests for cointegration are derived: The test statistics converge to…

分位数网络向量自回归中的双向同质性分组AI 解读

We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interactions. Motivated by prior studies on network effects in directed networks, our model assigns each node two latent group memberships to flexibly capture asymmetric and heterogeneous interactions among users. These memberships, along with model parameters, can be consistently estimated using the…

空间动态面板的再中心化矩估计量AI 解读

This paper proposes a recentered method of moments (RMM) approach for estimating spatial dynamic panel data (SDPD) models with both individual and time fixed effects. This approach directly exploits the correlation of regressors and disturbances, where heteroskedasticity may exist across units and/or over time. It is computationally tractable and can handle situations where other methods are not applicable. We show that the proposed estimator is consistent and asymptotically…

用户指定异方差参数模型下的回归有效估计AI 解读

We show that it is possible to estimate regression coefficients at least as precisely as Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and its recently proposed refinements, regardless of the user’s parametric model for heteroskedasticity. The key is to modify WLS depending on scalar target parameters, e.g., coefficients, marginal effects, predictions, etc. to obtain a targeted WLS that maximizes precision of estimation of the target given the user’s model for…

高维异质数据的无模型分布稳健特征筛选与错误发现控制AI 解读

In this paper, we propose a model-free feature screening framework tailored for high-dimensional and heterogeneous datasets, based on a novel distributionally robust dependence measure termed Copula Divergence. The proposed screening method, named CD-Screen, addresses critical limitations of existing feature screening methods, such as restrictive modeling assumptions and sensitivity to heterogeneous feature distributions. CD-Screen ranks features according to their Copula…

识别失败与模型设定正确性的协调检验AI 解读

We develop a specification test for GMM and Minimum Distance (MD) inference that is: (i) robust to weak identification; (ii) easy to implement and broadly applicable; and (iii) has meaningful power. Our specification test is based on a conditional inference approach: information about the existence of strongly identified directions in the parameter space is exploited to define a data-dependent critical value, which, in turn, increases the power of the resulting specification…

测地线双重差分AI 解读

Summary Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes residing in non-Euclidean spaces remain limited. Existing methods for such outcomes have primarily focused on univariate distributions, leveraging linear operations in the space of quantile functions, but these approaches cannot be directly extended to outcomes in general metric spaces. In…

Model-X Knockoffs 的渐近错误发现率控制：矩匹配是否足够？AI 解读

We propose a unified theoretical framework for studying the robustness of the model-X knockoffs framework by investigating the asymptotic false discovery rate (FDR) control of the practically implemented approximate knockoffs procedure. This procedure deviates from the model-X knockoffs framework by substituting the true covariate distribution with a user-specified distribution that can be learned using in-sample observations. By replacing the distributional exchangeability…

本地分布式多网络社区检测的隐私保护迁移学习AI 解读

Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose \texttt{TransNet}, a new spectral clustering-based transfer learning framework that improves community detection on a \emph{target network} by leveraging heterogeneous, locally stored, and privacy-preserved auxiliary \emph{source networks}. Our focus is the \textit{local differential privacy} regime, in which each local data provider…

高维分位数回归的无模型特征筛选与错误发现控制AI 解读

High-dimensional quantile regressions are powerful tools for capturing the diverse relationships between predictors and response variables across different quantile levels. However, existing feature screening methods for quantile regressions often rely on restrictive model assumptions, limiting their applicability in complex data settings. In this paper, we propose a novel model-free feature screening framework for high-dimensional quantile regressions, leveraging a novel…

将预测作为替代结果：人工智能时代重新审视替代结局AI 解读

Abstract We establish a formal connection between the decades-old surrogate outcome model in biostatistics and economics and the emerging field of prediction-powered inference. The connection treats predictions from pre-trained models, prevalent in the age of AI, as cost-effective surrogates for expensive outcomes that are not fully observed. Building on the surrogate outcomes literature, we develop recalibrated prediction-powered inference, a more efficient approach to…

Summary We consider likelihood-ratio-type statistics for testing restrictions on parameters built from a convex M-estimation criterion. In general, these statistics are not asymptotically pivotal. We propose a general simple non-parametric bootstrap procedure that yields asymptotically valid critical values and p-values. The method modifies the bootstrap objective function to mimic what happens under the null hypothesis. A Monte Carlo study illustrates that our method…

STRAP：利用随机投影的多元同质性平滑检验AI 解读

In this paper, we propose STRAP (Smooth Tests using RAdom Projections), projected smooth tests for the equality of two multivariate distributions. We employ random projections to map the multivariate data into one-dimensional data and transform the projected data via the probability integral transformation. Under the null hypothesis of homogeneity, the transformed data follow the standard uniform distribution, reducing the problem to testing for uniformity. Building on…

多个内生解释变量下 2SLS 的稳健弱工具变量检验AI 解读

Abstract We develop a test for instrument strength based on the bias of two-stage least squares (2SLS) that (1) generalizes Stock and Yogo’s and Sanderson and Windmeijer’s tests to be robust to heteroskedasticity and autocorrelation, and (2) extends Montiel Olea and Pflueger’s robust test for models with a single endogenous regressor to multiple endogenous regressors. Our test can be based either on an absolute bias criterion or on the 2SLS bias relative to a worst-case…

重返办公室政策与员工任职年限分布AI 解读

Abstract Debates over return-to-office mandates have intensified since the COVID-19 pandemic ended, though their economic implications remain poorly understood. Using large-scale resume data, we analyze how these policies affect employee tenure and seniority at three large U.S. tech companies: Microsoft, SpaceX, and Apple. Employing a distributional synthetic controls framework, we estimate a reduction in tenure and seniority at firms returning to the office. We extend the…

极端事件估计的分布鲁棒优化框架AI 解读

Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and theoretically grounded, could however encounter a difficult bias-variance tradeoff that exacerbates especially when data size is too small, deteriorating the reliability of the tail estimation. In this paper, we study a framework based on the recently surging literature of distributionally robust…

结合源分布混合的引导式对抗鲁棒迁移学习AI 解读

Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on a new one, reducing the need for extensive data and training in each new context. Many existing transfer learning methods rely on leveraging information from source populations closely resembling the target population. However, this approach often overlooks valuable knowledge that may be present in different yet potentially…

可观测因子与潜在因子AI 解读

We test for temporal stability in local linear projection coefficients of observable factors on latent ones embedded in a high-dimensional vector obeying a linear factor model. The proposed test explores the fact that, under the null hypothesis, residuals from global linear projections of observable factors on latent ones, computed over a fixed time interval via Principal Component Analysis (PCA), should also be locally uncorrelated with the PCA factors. The test is fully…

贝叶斯时空变化边界识别AI 解读

Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the values of the random field (or spatial surface indexed continuously over time) at arbitrary space-time coordinates, scientific interest often turns to gleaning information regarding zones of rapid spatial-temporal change. We develop Bayesian modeling and inference for directional rates of change…