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高维广义线性模型部分惩罚检验的强 Oracle 保证AI 解读

Abstract Partial penalized tests provide flexible approaches to testing linear hypotheses in high-dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially nonconvex folded-concave penalized objectives, the solutions one computes in practice may not coincide with the unknown local minima for which we have nice theoretical guarantees. To close this gap between theory and computation, we introduce local…

具有错误覆盖率控制的信息性共形预测集选择AI 解读

Abstract In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come after a selection process. The selection process requires that the selected prediction sets be ‘informative’ in a well-defined sense. We consider both the classification and regression settings where the analyst may…

贝叶斯惩罚经验似然与马尔可夫链蒙特卡洛抽样AI 解读

Abstract In this study, we introduce a novel methodological framework called Bayesian penalized empirical likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo sampling schemes as a convenient alternative to…

具有条件覆盖保证的共形预测AI 解读

Abstract We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples. Thus, most popular methods only guarantee marginal coverage over the covariates or are restricted to a limited set of conditional targets, e.g. coverage over a finite set of prespecified subgroups. This paper bridges this gap by defining a…

提高观察性区组设计敏感度的条件化策略AI 解读

Abstract In an observational block design, there are I blocks of J individuals, typically with one treated individual and J−1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It…

面向处理效应异质性学习的自适应实验AI 解读

Abstract Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the…

半参数后验修正AI 解读

Abstract We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization, and predictive power of nonparametric Bayesian procedures to estimate low-dimensional functionals of interest without being restricted by the holistic Bayesian formalism. Starting from a conventional posterior on the whole data-generating distribution, we correct the marginal posterior for each functional of…

两阶段拒绝式抽样及其渐近性质AI 解读

Abstract Rejective sampling improves design and estimation efficiency of single-phase sampling when auxiliary information in a finite population is available. When such auxiliary information is unavailable, we propose to use two-phase rejective sampling (TPRS), which involves measuring auxiliary variables for the sample of units in the first phase, followed by the implementation of rejective sampling for the outcome in the second phase. We explore the asymptotic design…

数据增强不变的流形学习AI 解读

Abstract Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications. Despite the empirical effectiveness, most existing methods lack theoretical understanding under a general nonlinear setting. To fill this…

一般协方差下超高维均值的随机化经验似然检验AI 解读

Abstract This article proposes a calibrated empirical likelihood test for ultra-high dimensional means that incorporates multiple projections. Under weak moment conditions on the distributions of data, we analyse all possible asymptotic distributions of the proposed test statistic in different scenarios. To determine the critical value and enhance test power, we employ the random symmetrization method based on the group of sign flips and use multiple selected projections.…

高斯变分近似中Cholesky因子的解析自然梯度更新AI 解读

Abstract Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in Gaussian variational approximation, natural gradient updates of the precision matrix do not ensure positive definiteness. To tackle this issue, we derive analytic natural gradient updates of the Cholesky factor of the covariance or precision matrix and consider sparsity constraints…

交互项模型中的遗漏变量偏误：一则警示AI 解读

Abstract We highlight that analyses using interaction terms to study treatment effect heterogeneity are susceptible to a form of omitted variable bias that is often overlooked in economics. Unlike most instances of omitted variable bias, the omitted variables in this case are available to the researcher but were not included in the model. We demonstrate that this exclusion matters based on a replication of 205 estimates across seventeen papers published in the American…

贝叶斯局部投影AI 解读

Abstract We propose a Bayesian approach to Local Projections (LPs) that optimally addresses the empirical bias-variance trade-off intrinsic in the choice between direct and iterative methods. Bayesian Local Projections (BLPs) regularize LP regressions via informative priors and estimate impulse response functions that capture the properties of the data more accurately than iterative VARs. BLPs preserve the flexibility of LPs while retaining a degree of estimation uncertainty…

选举中的执政更替AI 解读

Abstract In most national elections, voters face a key choice between continuity and change. Electoral turnovers occur when the incumbent candidate or party fails to win reelection. To understand how turnovers affect national outcomes, we study all presidential and parliamentary elections held globally between 1946 and 2018. We document the prevalence of turnovers over time and estimate their effects on economic performance, human development, and the quality of democracy.…

校准参数的标准误AI 解读

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive…

时变参数MIDAS模型：美国实际GDP即时预测应用AI 解读

We introduce a novel time-varying parameter mixed-data sampling (TVP-MIDAS) framework. Specifically, we allow both the MIDAS weights and the coefficients representing the overall impacts of the high-frequency variables to vary over time. This is done by introducing a class of linear parameterizations, which facilitate estimation in settings with a large number of high-frequency predictors. We demonstrate the usefulness of this framework via an application of nowcasting US…

支持向量决策方法AI 解读

The paper develops a support vector machine (SVM) for binary decision-making within a utility framework. Given an information set, a decision-maker first predicts a binary outcome and then selects a binary action based on this prediction to maximize expected utility, where the utility function can depend on the action taken, observable covariates, and the binary outcome subsequently realized. The proposed maximum utility SVM differs from the traditional SVM in four key…

平均处理效应的回归调整插补估计AI 解读

Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier…

多重结构突变与多因子误差下空间面板模型的收缩估计AI 解读

This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a…

半参数单指标变换模型的识别与推断AI 解读

This paper considers a semiparametric single index model in which the dependent variable is subject to a nonparametric transformation. The model has the form G0(Y)=g0(X⊤θ0)+e, where X is a random vector of regressors, Y is the dependent variable and e is the random noise, the monotonic function G0, the smooth function g0 and the index vector θ0 are all unknown. This model is quite general in the sense that it nests many popular regression models as special cases. We first…

人工智能驱动的特征价格与质量调整价格指数AI 解读

We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or “features”) from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon’s…

非线性因子模型中的深度学习残差：低信噪比收益精度矩阵估计AI 解读

This paper introduces a consistent estimator and rate of convergence for the precision matrix of asset returns in large portfolios using a non-linear factor model within the deep learning framework. Our estimator remains valid even in low signal-to-noise ratio environments typical for financial markets and is compatible with the weak factor framework. Our theoretical analysis establishes uniform bounds on expected estimation risk based on deep neural networks for an…

不依赖序列排序的得分驱动动态因子模型AI 解读

This paper introduces a novel score-driven dynamic factor model designed for filtering cross-sectional co-movements in panels of time series. The model is formulated using elliptical distribution for noise terms, allowing the update of the time-varying parameter to be potentially nonlinear and robust to outliers. We derive stochastic properties of time series generated by the model, such as stationarity and ergodicity, and establish the invertibility of the filter. We prove…

用于预测组合的 Bregman 模型平均AI 解读

We propose a unified model averaging (MA) approach for a broad class of forecasting targets. This approach is established by minimizing an asymptotic risk based on the expected Bregman divergence of a combined forecast, relative to the optimal forecast of the forecasting target, under local(-to-zero) asymptotics. It can be flexibly applied to develop effective MA methods across various forecasting contexts, including but not limited to univariate and multivariate mean…