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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…

宏观经济新闻冲击与意外冲击的估计AI 解读

ABSTRACT A common VAR approach is to identify responses to TFP news shocks by maximizing the variance share of TFP over a long horizon. We find that these TFP max share estimators tend to be biased in large samples when applied to data generated from DSGE models with shock processes that match TFP moments in the data, especially in the presence of TFP measurement error. We propose an alternative max share news estimator that reduces this bias and the RMSE of the impulse…

含时变协变量的直接与间接处理效应AI 解读

ABSTRACT We propose a simple approach to treatment effect estimation in panel data that is valid when the number of time periods is small and the parallel trends condition is violated due to the presence of interactive fixed effects. The procedure allows the covariates to be affected by treatment and enables separation of the part of the estimated treatment effect that is due to the covariates from the part that is not. The asymptotic properties of the new approach are…

因子增强回归预测准确性的稳健检验：新欧元区 EA-MD-QD 数据集应用AI 解读

ABSTRACT We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural breaks in the…

可靠性随时间变化的高频工具变量：理解宏观经济识别AI 解读

ABSTRACT The effects of monetary policy shocks are regularly estimated using high‐frequency surprises in asset prices around central bank meetings as an instrument. These studies, insofar as they explicitly model the relationship between instrument and structural shock, assume a constant relationship between the instrument and the monetary policy shock. By allowing for time variation in this relationship, we show that only a few distinct periods are informative about…

高频信息组合优化中重审 EWMA：比较评估AI 解读

ABSTRACT This paper compares the statistical and economic performance of state‐of‐the‐art high‐frequency (HF) based multivariate volatility models with a simpler, widely used alternative, the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002–2023), we assess model performance through a Global Minimum Variance portfolio optimization exercise, with and without short‐selling restrictions across multiple forecast…

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…

一般处理模型的高阶去偏估计量AI 解读

We have witnessed tremendous progress in developing the foundation for econometrics and causal inference in the past decades. The most popular paradigm is the classical (first-order) semiparametric theory, in which a key building block is the (first-order) influence function. However, it is well known that estimators based on influence functions can be sub-optimal in various settings. To address this issue, higher-order influence functions (HOIF) are developed. However, most…

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…

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…

自适应采集数据的随机化检验AI 解读

Randomization testing is a fundamental method in statistics, enabling inferential tasks such as testing for (conditional) independence of random variables, constructing confidence intervals in semiparametric location models, and constructing (by inverting a permutation test) model-free prediction intervals via conformal prediction. Randomization tests are exactly valid for any sample size, but their use is generally confined to exchangeable data. Yet in many applications,…

异质站点下单轮通信的分布式半参数推断AI 解读

Distributed inference has proven useful in contexts where data privacy is the central concern. However, privacy constraints often necessitate cumbersome coordination to exchange summary data during distributed learning. Most existing methods require two rounds of data communication, yet excessive communication rounds might discourage participation from local sites. This study develops distributed algorithms that require only a single communication round while maintaining…

通过广义 Dantzig 选择器构建可解释的标量对图像线性回归AI 解读

The scalar-on-image regression model examines the association between a scalar response and a bivariate function (e.g., images) through the estimation of a bivariate coefficient function. Existing approaches often impose smoothness constraints to control the bias-variance trade-off, and thus prevent overfitting. However, such assumptions can hinder interpretability, especially when only certain regions of an image influence changes in the response. In such a scenario,…

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,…

利用不完美辅助信息增强局部共形预测AI 解读

There is growing interest in constructing conformal prediction sets that provide approximate or asymptotic conditional coverage guarantees, capturing local data heterogeneity. However, methods like localized conformal prediction (LCP) may face challenges in ensuring reliable prediction sets in regions with sparse calibration data. This paper introduces Enhanced Localized Conformal Prediction (ELCP), a novel approach that incorporates auxiliary data to refine localized…

通过剖面化迁移学习消除混杂AI 解读

Unmeasured confounders are a major source of bias in regression-based effect estimation and causal inference. In this paper, we propose a new profiled transfer learning framework, ProTrans, to address confounding effects in the target dataset, when additional source datasets with similar confounding structures are available. We introduce the concept of profiled residuals to characterize the shared confounding patterns between source and target datasets. By incorporating…

函数型数据变化率的在线学习AI 解读

With advances in modern data collection technologies, functional data are increasingly received in a streaming manner. Although numerous methods have been developed to model such data, most focus on estimating the mean and covariance functions, while giving limited attention to their derivatives. In this paper, we introduce an online method for estimating the change rates of the mean and covariance functions, enabling real-time updates with high statistical efficiency. 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…

一类半参数多项选择模型的闭式估计量AI 解读

We consider a semiparametric multinomial choice model that allows for an arbitrary joint distribution of choice specific unobservables that are independent of explanatory variables. This model permits relatively flexible substitution patterns between choices. To minimize computational difficulties, we restrict attention on estimators of the model that can be expressed in closed form. We combine and extend various results from the existing literature to enforce economic…

极端高温持续性的估计与推断AI 解读

Summary: We propose a non-parametric framework for estimating the extremal index that captures the persistence of extreme observations. The framework provides unified and simple procedures for verifying the well-known local dependence condition $D^{(d)}(u_{n})$, which characterizes the extremal index yet is often assessed through heuristic checks, and for selecting d (a key parameter for estimation) when the condition holds. Under a general $\phi$-mixing condition, we…

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…