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无混杂与共同趋势的联合检验AI 解读

ABSTRACT We introduce an overidentification test of two alternative assumptions to identify the average treatment effect on the treated in a two‐period panel data setting: unconfoundedness and common trends. Under unconfoundedness, treatment assignment and post‐treatment outcomes are independent, conditional on control variables and pre‐treatment outcomes, which motivates including the pre‐treatment outcomes in the set of controls. Under common trends, the trend and the…

以倾向得分校准改善双重／去偏机器学习的平均处理效应有限样本估计AI 解读

ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates. Estimators relying on double‐robust score functions are highly sensitive to errors in propensity score predictions. Machine learning algorithms have been found to produce models that often…

通过稀疏性识别因子扩展 VAR 模型AI 解读

This paper introduces a regularized factor-augmented vector autoregressive (RFAVAR) model which incorporates sparsity in the factor loadings. Within this framework, the factors can load on a subset of variables, thereby enabling factor identification and enhancing their economic interpretation. The proposed RFAVAR model allows to investigate the effects of structural shocks on economically interpretable factors and on all observed time series included in the model. We prove…

宏观审慎政策的尾部传导：叙事识别证据AI 解读

ABSTRACT We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth for advanced European economies using a narrative‐identification strategy in a quantile‐regression framework. While macroprudential policy has near‐zero effects on the center of the GDP‐growth distribution, tighter policy brings benefits by reducing the variance of future growth, significantly boosting the left tail while simultaneously reducing the right. Assessing a…

存在缺失数据时的深度学习AI 解读

Abstract In the context of multivariate nonparametric regression with missing covariates, we propose pattern embedded neural networks (PENNs), which can be applied in conjunction with any existing imputation technique. In addition to a neural network trained on the imputed data, PENNs pass the vectors of observation indicators through a second neural network to provide a compact representation. The outputs are then combined in a third neural network to produce final…

函数型时间序列共同趋势的统计推断AI 解读

We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, that is, the dimension of the nonstationary subspace. We also consider tests of hypotheses on the nonstationary and stationary subspaces themselves. The Hilbert space can be of an arbitrarily large dimension, and our methods remain asymptotically valid even when the time…

树结构大规模稀疏线性方程组及其在光纤网络中的应用AI 解读

Fiber optic networks require precise time synchronization, yet asymmetric edges introduce delays that challenge accurate correction. Current solutions like manual detection or the deployment of Global Navigation Satellite System (GNSS) receivers are impractical due to high costs and labor requirements. Statistically, delay detection is complicated by non-identifiability, fixed tree topologies, and absent structured sparsity assumptions. To overcome these difficulties, we…

生成式人工智能辅助因果推断：以文本作为处理AI 解读

In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation…

广义潜在因子模型的可识别性与推断AI 解读

Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning, but also serves as a widely used tool across various scientific fields, such as psychometrics, econometrics, and social sciences. Ensuring the identifiability of latent factors and the loading matrix is essential for the model’s estimability and interpretability, and various identifiability conditions have been employed by practitioners. However,…

高维数据整合中的最优网络引导协变量选择AI 解读

Modern data often arise with multiple modalities. For example, covariates and a network are observed on the same subjects, and both contain useful information. Effectively integrating these modalities is important and challenging, especially when the response is unavailable. We study the fundamental covariate selection problem for high-dimensional data by leveraging network information.We propose the Network-Guided Covariate Selection (NGCS) algorithm. NGCS exploits the…

高维时间序列因子模型的权重校准估计AI 解读

The factor modeling for high-dimensional time series is powerful in discovering latent common components for dimension reduction and information extraction. Most available estimation methods can be divided into two categories: the covariance-based under asymptotically-identifiable assumption and the autocovariance-based with white idiosyncratic noise. This paper follows the autocovariance-based framework and develops a novel weight-calibrated method to improve the estimation…

用户级局部差分隐私的速率最优性与相变AI 解读

Most literature on differential privacy considers the item-level case where each user has a single observation, but a growing field is that of user-level privacy where each of the n users holds T observations and wishes to maintain the privacy of their entire collection. We derive a general minimax lower bound, which shows that, for locally private user-level estimation problems, the risk cannot, in general, be made to vanish for a fixed n even for T arbitrarily large. We…

广义自回归得分树与森林AI 解读

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal, et al., 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultaneously. We apply the new models to four distinct empirical analyses, and in all applications the proposed new methods…

动态矩阵因子模型：协变量驱动的时变载荷AI 解读

This paper proposes a covariate-driven matrix factor model in which the row and column loading matrices evolve as nonparametric functions of an observable time-varying covariate sequence. This flexible specification accommodates dynamic factor loadings driven by the temporal characteristics of the covariate. We develop a nonparametric estimation procedure that combines kernel smoothing with principal component analysis (PCA). Under high-dimensional asymptotics, we establish…

企业集中度与工作设计：弹性工时安排的案例AI 解读

Abstract We study how employer concentration affects job design, focusing on schedule flexibility as a non-wage job attribute. Using the full text of 46 million UK job vacancies from 2014–2019, we construct new measures of flexible work arrangements using supervised machine learning and large-language-model—assisted annotations. Adopting an instrumental variable strategy using a Bartik-style prediction of the change in employer concentration based on nationwide hiring…

气候常态与异常值的估计AI 解读

Summary The quantification of the interannual component of variability in climatological time series is essential for the assessment and prediction of the El Niño–Southern Oscillation phenomenon. This is achieved by estimating the deviation of a climate variable from normal conditions, defined by its baseline level and seasonal patterns. Climate normals are currently estimated by simple arithmetic averages calculated over the most recent 30-year period ending in a year…

存在内生性的多门槛回归估计与推断AI 解读

Heterogeneity and endogeneity become increasingly common in statistical modeling and econometric practice. Threshold regression provides a simple yet flexible modeling strategy to account for heterogeneity. This paper studies estimation and inference for multi-threshold regression with endogenous regressors. We exploit a novel two-stage estimation procedure based on instrumental variables, which first identifies a set of possible threshold locations using the group LASSO…

面向稀疏模式异质性的多任务学习：统计与计算视角AI 解读

Abstract We consider a problem in multi-task learning (MTL) where multiple linear models are jointly trained on a collection of datasets (‘tasks’). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging (1) coefficient supports,…

Mondrian 随机森林的统计推断AI 解读

Abstract Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondrian random forest, in which the underlying constituent trees are constructed via a Mondrian process. We give precise bias and variance characterizations, along with a Berry–Esseen-type central limit theorem, for the Mondrian random forest regression estimator. By combining these results…

弱识别函数的强识别泛函推断AI 解读

Abstract In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and analysis of missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g. average causal effects) of nuisance functions (e.g. NPIV regression) defined by conditional moment restrictions. These nuisance functions are often weakly identified, meaning the…

风险最小化参数的广义通用推断AI 解读

Abstract A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications. The present paper considers a very common case in machine learning, where the quantity of interest is the…

作为线性回归的增广平衡权重AI 解读

Abstract We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights—weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single…

高斯不变马尔可夫链蒙特卡洛AI 解读

We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA. Unlike standard RWM and MALA, we show that Gaussian invariant sampling can lead to ergodic estimators with improved statistical efficiency. This is due to a remarkable property of Gaussian invariance that allows us to obtain exact analytical solutions to the Poisson equation for…

《空间密度建模：统计、计量经济学与机器学习中的数据、方法及 R 应用》书评条目AI 解读

Katarzyna Kopczewska. New York, NY: Oxford University Press, 2025, xii + 307 pp., $38.99(e-book), ISBN: 978-0-198-97518-2.Modelling Spatial Density: Data, Methods, and R Applications in Statistics ...