论文检索

预测组合：为什么简单平均胜过最优线性权重AI 解读

A continuing puzzle in constructing a point forecast by combining individual forecasts is that simple averaging often leads to a smaller out-of-sample mean square error compared to estimating optimal weights (the forecast combination puzzle). Most researchers have suggested that the puzzle is explained by estimation error in computing the weights, typically by estimation of a restricted least squares regression. For this explanation, gains from optimal weights must be small.…

凸成对差分估计量的稳健推断AI 解读

This article develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs with similar covariates, where the similarity is governed by a localization (bandwidth) parameter. While classical results establish asymptotic normality under restrictive bandwidth conditions, we show that valid Gaussian and bootstrap-based…

局部单位根回归的自加权估计及应用AI 解读

A new self-weighted (SW) least squares (LS) estimation approach is developed for local unit root (LUR) autoregression with conditionally heteroskedastic innovations. Asymptotically controlled trimming of the most extreme observations in LS regression is shown to lead to mixed Gaussian limit theory and studentization produces standard normality free of the unknown LUR parameter. The estimator is super consistent with a convergence rate slightly below the O P ( n ) $O_P(n)$ upp…

任意带宽选择下的预测模型权重形状优化AI 解读

Parameter instability has been widely documented to lead to poor predictive accuracy for economic forecasting models. Adaptive schemes for data weighting, such as rolling-window estimation, only involve a single tuning parameter (akin to a bandwidth) and have been widely used to address this issue. The importance of the shape of the weighting scheme (kernel) has received far less attention in the forecasting literature. In this paper we show that in practice there can be a…

用于实验设计的合成控制方法AI 解读

Abstract This paper studies how to design experiments when the experimental units are large aggregate entities (e.g., markets), and only one or a small number of units can be treated. We propose a class of non-randomized experimental designs using the synthetic control method, which jointly selects treated units and untreated units to serve as a comparison group. We analyze the properties of estimators under these synthetic control designs and develop new inferential…

沙威的怀疑有道理吗？持续时间结果的边际处理效应AI 解读

Abstract We identify the distributional and quantile marginal treatment effect functions when the outcome is right-censored. Our method requires a conditionally exogenous instrument and random censoring. We propose asymptotically consistent semi-parametric estimators and valid inferential procedures for the target functions . To illustrate, we evaluate the effect of alternative sentences (fines and community service vs. no punishment) on recidivism in Brazil. Our results…

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…

社会互动离散选择模型中的分位数效应AI 解读

Abstract This paper provides a method to study quantile effects in discrete choice with social interactions. The method is based on a behavioral social interactions model from quantile preference in decision making and demonstrates peer effects on different quantiles of discrete outcomes. The peer effects parameters are estimated by a nested pseudoscore (NPS) approach, which is developed to tackle the computational burden pertaining to the social interactions model.…

单因子或双因子低维因子模型中的弱识别AI 解读

Abstract This paper describes how to reparametrize low-dimensional factor models with one or two factors to fit weak identification theory developed for generalized method of moments models. Some identification-robust tests, here called “plug-in” tests, require a reparametrization to distinguish weakly identified parameters from strongly identified parameters. The reparametrizations in this paper make plug-in tests available for subvector hypotheses in low-dimensional factor…

跨期处理效应的双重差分估计AI 解读

Abstract We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commonly used two-way…

基于最优传输的多元分布无关检验的 Pitman 效率下界AI 解读

Abstract The Wilcoxon rank sum test is one of the most popular distribution-free two-sample tests for univariate data. Among the important reasons for their popularity are the striking results of Hodges–Lehmann and Chernoff–Savage, where the authors show that the asymptotic (Pitman) relative efficiency of Wilcoxon’s test compared to Student’s t-test, never falls below 0.864 (with identity score) and 1 (with Gaussian score), respectively. Motivated by these results, we…

多重随机化设计：存在干扰时的估计与推断AI 解读

Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for ‘Simple Multiple…

控制错误发现率的联邦特征选择AI 解读

Abstract Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this…

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…

Abstract Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency…

带人工动态的自组织状态空间模型AI 解读

Abstract We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter θ. A popular idea to address this problem consists of incorporating θ in the state of the system and allowing its time evolution, modelled as a Markov chain (θt)t≥1. This proxy model defines a so-called self-organizing SSM (SO-SSM) to which one may apply standard particle filters. However, the practical implementation of this idea in a…

分布式差分隐私约束下非参数分类的极小极大与自适应迁移学习AI 解读

Abstract This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source…

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…