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非线性 DSGE 模型的有限样本识别稳健推断AI 解读

ABSTRACT We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projections. Proposed…

基于局部投影的双重差分方法AI 解读

ABSTRACT We propose a local projections (LPs)‐based difference‐in‐differences (DiD) approach that subsumes many of the recent solutions proposed in the literature to address possible biases arising from negative weighting. We combine LPs with a flexible “clean control” condition to define appropriate sets of treated and control units. Our proposed LP‐DiD estimator can be implemented with various weighting and normalization schemes for different target estimands, can be…

双重差分因果森林及其在挪威工资税负担归宿中的应用AI 解读

ABSTRACT This paper introduces the difference‐in‐difference causal forest (DiDCF) method, which extends the causal‐forest technique for estimating heterogeneous treatment effects to settings with dynamic treatment effects. Regular causal forests require independence between treatment assignment and the outcome variable (after conditioning out observables). In contrast, DiDCFs provide consistent estimates with a parallel trend assumption. DiDCFs can be used to create…

公共项目何时应由私人机构执行？工资保障计划的理论与证据AI 解读

Abstract When should private companies allocate public resources? In our model, delegation is attractive when delay is costly, the impact of funds is similar across firms, and government and private objectives are aligned. We use novel firm-level survey data to measure heterogeneity in the impact of the Paycheck Protection Program and to assess whether banks targeted loans to high-impact firms. Banks did target loans to their most valuable pre-existing customers. However, we…

特定技能信号与大学毕业生劳动力市场AI 解读

Abstract Using administrative data and a regression discontinuity design, we study the labor market effects of a government-sponsored award given to top-performing students on Colombia’s national college exit exam. The award signals field-specific skills, leading recipients to earn seven to ten percent more than comparable peers without the signal. The benefits are concentrated among graduates from lowerreputation institutions, who enter the market with weaker signals and…

利用部分网络数据估计同伴效应AI 解读

Abstract We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer…

加性模型下异质处理效应估计量的模型平均AI 解读

We consider spline-based additive models for estimation of conditional treatment effects. To handle the uncertainty due to variable selection, we propose a method of model averaging with weights obtained by minimizing a J -fold cross-validation criterion, in which a nearest neighbor matching is used to approximate the unobserved potential outcomes. We show that the proposed method is asymptotically optimal in the sense of achieving the lowest possible squared loss in some…

张量因子模型中Kronecker积结构的检验AI 解读

Abstract We propose a test for the Kronecker product structure of a factor loading matrix implied by a tensor factor model with Tucker decomposition in the common component. By defining a Kronecker product structure set, we determine whether a tensor time series has a Kronecker product structure, equivalent to its ability to decompose the series according to a tensor factor model. Our test is built on analysing and comparing the residuals from fitting a full tensor factor…

灵活匹配设计下观察性研究的敏感性分析AI 解读

Abstract Observational studies provide invaluable opportunities to draw causal inference, but they may suffer from biases due to pretreatment differences between treated and control units. Matching is a popular approach to reduce observed covariate imbalance. To tackle unmeasured confounding, a sensitivity analysis is often conducted to investigate how robust a causal conclusion is to the strength of unmeasured confounding. For matched observational studies, Rosenbaum…

二阶无偏贝叶斯估计量的先验构造AI 解读

Summary Asymptotically unbiased priors, introduced by Hartigan (1965), are designed to achieve second-order unbiasedness of Bayes estimators. This paper extends Hartigan’s framework to non-independent-and-identically-distributed models by deriving a system of partial differential equations that characterizes asymptotically unbiased priors. Furthermore, we establish a necessary and sufficient condition for the existence of such priors and propose a simple procedure for…

积分方程解的线性泛函置信集合的渐近有效性AI 解读

Summary This paper examines the construction of confidence sets for parameters defined as linear functionals of a function of $ W $ and $ X $ whose conditional mean given $ Z $ and $ X $ equals the conditional mean of another variable $ Y $ given $ Z $ and $ X $. Many estimands of interest in causal inference can be expressed in this form, including the average treatment effect in proximal causal inference and treatment effect contrasts in instrumental variable models. We…

一般抽样设计下小区域预测的替代均方误差估计与置信区间AI 解读

Abstract Estimating the mean square error of a small area predictor under an informative sampling design is a challenging problem. Existing approaches rely on approximations that have not been justified theoretically. We provide rigorous support for a mean square error estimator that is applicable to an informative sample design. The procedure can be used in combination with predictors of general parameters that may be nonlinear functions of the model response variable. We…

有监督污染检测及其流式细胞术应用AI 解读

Summary The contamination detection problem aims to determine whether a set of observations has been contaminated, i.e., whether it contains points drawn from a distribution different from the reference distribution. Here, we consider a supervised problem, where labelled samples drawn from both the reference distribution and the contamination distribution are available at training time. This problem is motivated by the detection of rare cells in flow cytometry. Compared to…

俄罗斯轮盘赌：效用依赖反事实时为何需要随机潜在结果AI 解读

Summary It has been proposed in medical decision analysis to express the ‘first, do no harm’ principle as an asymmetric utility function in which the loss from killing a patient would count more than the gain from saving a life. Such a utility depends on unrealized potential outcomes, and we show how this yields a paradoxical decision recommendation in a simple hypothetical example involving games of Russian roulette. The problem is resolved if we abandon the stable unit…

多元高斯累积分布函数与对偶贝叶斯 probit 模型的边际似然AI 解读

Summary The computation of multivariate Gaussian cumulative distribution functions is a key step in many statistical procedures, often representing a crucial computational bottleneck. Over the past few decades, efficient algorithms have been proposed to address this problem, mainly using Monte Carlo solutions. This work highlights a connection between the multivariate Gaussian cumulative distribution function and the marginal likelihood of a tailored dual Bayesian probit…

网络关联数据的高维因子分析AI 解读

Summary Factor analysis is a statistical tool widely used in many disciplines, such as psychology, economics and sociology. As observations linked by networks become increasingly common, incorporating network structures into factor analysis is an important problem that remains open. This article focuses on high-dimensional factor analysis involving network-connected observations, and we propose a generalized factor model with latent factors that account for both the network…

多重插补数据的重抽样方法AI 解读

Abstract Resampling techniques have become increasingly popular for estimation of uncertainty. However, data are often fraught with missing values that are commonly imputed to facilitate analysis. This article addresses the issue of using resampling methods such as a jackknife or bootstrap in conjunction with imputations that have been sampled stochastically, in the vein of multiple imputation. We derive the theory needed to illustrate two key points regarding the use of…

无限联合物种分布模型AI 解读

Summary Joint species distribution models are popular in ecology for modelling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multivariate binary indicators of the occurrences of different species in each sample, along with sample-specific covariates. A key problem is that current models implicitly assume that the list of species under consideration is predefined and finite, while for highly diverse groups of…

精确平衡的有限总体样本：存在性与应用AI 解读

Abstract Samples selected from finite populations can rarely be exactly balanced, as sample selection is an integer problem and the balancing equations are strict equalities. Selecting a balanced sample is not a problem limited to survey sampling. It also applies to design of experiments, clinical trials, causality, exact inference, graph theory and network analysis. Building on Jean-Claude Deville’s foundational work, we explore conditions under which exact solutions are…

更稳健的多变量孟德尔随机化方法AI 解读

Summary Multivariable Mendelian randomization uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, multivariable Mendelian randomization often faces greater challenges with many weak instruments, which can lead to bias not necessarily toward zero and inflation of Type-I errors. In this work, we introduce a new asymptotic regime that allows exposures to have…

逆卡尔曼滤波器AI 解读

Summary We introduce the inverse Kalman filter, which enables exact matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with linear computational cost. We integrate the inverse Kalman filter with the conjugate gradient algorithm, which substantially accelerates the computation of matrix inversion for a general form of covariance matrix, where other approximation approaches may not be directly applicable. We…

利用高维数据增广估计尖峰协方差模型的维度AI 解读

Summary We propose a modified, high-dimensional version of a recent dimension estimation procedure that determines the dimension via the introduction of augmented noise variables into the data. Our asymptotic results show that the proposal is consistent in wide, high-dimensional scenarios, and further shed light on why the original method breaks down when the dimension of either the data or the augmentation becomes too large. Simulations and real data are used to demonstrate…

贝叶斯多元逻辑回归模型中的因子预训练AI 解读

Abstract This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors in the linear predictor. Bayesian approaches are useful for characterizing uncertainty in inferring the regression coefficients, factors and loadings, while also incorporating hierarchical and shrinkage structures. However, Markov chain Monte Carlo algorithms for posterior computation…

线性非高斯结构方程模型的拟合优度检验AI 解读

Abstract The field of causal discovery develops model selection methods to infer cause-effect relations among a set of random variables. For this purpose, different modelling assumptions have been proposed to render cause-effect relations identifiable. One prominent assumption is that the joint distribution of the observed variables follows a linear non-Gaussian structural equation model. In this paper, we develop novel goodness-of-fit tests that assess the validity of this…