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具有大量弱工具变量的二元响应模型AI 解读

ABSTRACT This paper considers an endogenous binary response model with many weak instruments. We employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of which is called a regularized conditional maximum likelihood estimator (RCMLE) and a regularized nonlinear least…

美国货币政策与均衡不确定性AI 解读

ABSTRACT In this paper, I investigate the stance of the US monetary policy in the postwar period. To this end, I show that two features are key: a rich structural model and a parameterization of the indeterminate solution that ensures a sufficient exploration of the indeterminacy region during the estimation procedure. Therefore, I estimate a medium‐scale model on the US macroeconomic data using a novel solution method to allow for indeterminacy. The evidence of a passive…

识别经济增长放缓的来源：需求与供给AI 解读

ABSTRACT Long‐run GDP growth has declined in the United States over the past two decades. Two competing views take centre stage in accounting for this slowdown: the demand‐side view and the supply‐side view. I empirically quantify their relative importance in a Bayesian SVAR with common trends identified using sign restrictions. While supply‐side factors were the main driver of long‐run GDP growth prior to 2000, demand‐side factors explain half of its slowdown afterwards.…

重新评估稀疏网格积分的似然近似AI 解读

ABSTRACT This paper revisits sparse grid integration proposed in the literature for approximating integrals that occur as choice probabilities in random coefficient discrete choice models. First, we successfully replicate their main findings for the panel mixed logit. Second, for higher variances and for a different structure of the variances of the random coefficients, in certain cases, we fail to replicate the original results. Third, for the important special case of…

重新评估收益率利差对美国衰退的预测能力AI 解读

ABSTRACT Rudebusch and Williams (2009) predict recessions in the United States utilising a probit model with the lagged yield spread as a real‐time predictor. Mindful of the importance of recent yield curve movements, we update their analysis and evaluate quarterly forecasts from their probit model up to the end of 2023. We also analyze lagged financial conditions as an alternative real‐time predictor. We find that both the yield spread and financial conditions perform…

菲利普斯曲线的断点：来自面板数据的证据AI 解读

ABSTRACT We revisit the Phillips curve, applying new Bayesian panel methods with structural breaks to US and EU disaggregate data. Our approach lets us estimate both the number and timing of breaks and to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns. We find evidence of a flattening for US sectoral data and among EU countries, particularly poorer ones. Evidence of flattening is weaker for MSA‐level…

以大型因子增广 VAR 分析企业上下游与共同冲击AI 解读

ABSTRACT We evaluate the roles of upstream (supplier‐to‐user), downstream (user‐to‐supplier), and common factor shock transmission across firms by deriving interfirm networks and common factors from US equities over 1989–2017. We overcome the econometric challenges of estimating the large factor‐augmented vector autoregressive (FAVAR) system by developing two alternative approaches: one prioritizing computational efficiency and the other providing the full posterior…

保序子群选择AI 解读

Abstract Given a sample of covariate–response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a predetermined threshold. We introduce a computationally feasible approach for subgroup selection in the context of multivariate isotonic regression based on martingale tests and multiple testing procedures for logically structured hypotheses. Our proposed procedure satisfies a non-asymptotic,…

秩变换子抽样：多次数据拆分与可交换 p 值的推断AI 解读

Abstract Many testing problems are readily amenable to randomized tests, such as those employing data splitting. However, despite their usefulness in principle, randomized tests have obvious drawbacks. Firstly, two analyses of the same dataset may lead to different results. Secondly, the test typically loses power because it does not fully utilize the entire sample. As a remedy to these drawbacks, we study how to combine the test statistics or p-values resulting from…

连续索引图模型AI 解读

Abstract Let X={Xu}u∈U be a real-valued Gaussian process indexed by a set U. We show that X can be viewed as a graphical model with an uncountably infinite graph, where each Xu is a vertex. This graph is characterized by the reproducing property of X’s covariance kernel, without restricting U to be finite or countable, allowing the modelling of stochastic processes in continuous time/space. Unlike traditional methods, this characterization is not based on zero entries of an…

通过随机游走转移耦合对齐与比较有向网络AI 解读

Abstract We describe and study a transport-based procedure called network optimal transition coupling (NetOTC) for the comparison and alignment of two networks. The networks of interest may be directed or undirected, weighted or unweighted, and may have distinct vertex sets of different sizes. Given two networks and a cost function relating their vertices, NetOTC finds a transition coupling of their associated random walks having minimum expected cost. The minimizing cost…

按模态的主子空间追踪与矩阵尖峰协方差模型AI 解读

Abstract This paper introduces a novel framework called Mode-wise Principal Subspace Pursuit (MOP-UP) to extract hidden variations in both the row and column dimensions for matrix data. To enhance the understanding of the framework, we introduce a class of matrix-variate spiked covariance models that serve as inspiration for the development of the MOP-UP algorithm. The MOP-UP algorithm consists of two steps: Average Subspace Capture (ASC) and Alternating Projection. These…

扩展信赖推断：迈向自动化统计推断AI 解读

Abstract While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set—‘inferring the uncertainty of model parameters on the basis of observations’—has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable…

连续时间生存与事件史分析中边际因果效应识别的图形准则AI 解读

Abstract We consider continuous-time survival and event-history settings, where our aim is to graphically represent causal structures allowing us to characterize when a causal parameter is identified from observational data. This causal parameter is formalized as the effect on an outcome event of a (possibly hypothetical) intervention on the intensity of a treatment process. To establish identifiability, we propose novel graphical rules indicating whether the observed…

捕捉信号：利用 knockoff e 值进行信号定位AI 解读

Abstract We consider problems where many, somewhat redundant, hypotheses are tested and we are interested in reporting the most precise rejections, with false discovery rate (FDR) control. This is the case, for example, when researchers are interested both in individual hypotheses as well as group hypotheses corresponding to intersections of sets of the original hypotheses, at several resolution levels. A concrete application is in genome-wide association studies, where,…

协变量调整回归函数的去偏推断AI 解读

Abstract In this article, we study nonparametric inference for a covariate-adjusted regression function. This parameter captures the average association between a continuous exposure and an outcome after adjusting for other covariates. Under certain causal conditions, it also corresponds to the average outcome had all units been assigned to a specific exposure level, known as the causal dose–response curve. We propose a debiased local linear estimator of the…

通过非参数极大似然实现多元异方差经验贝叶斯估计AI 解读

Abstract Multivariate, heteroscedastic errors complicate statistical inference in many large-scale denoizing problems. Empirical Bayes is attractive in such settings, but standard parametric approaches rest on assumptions about the form of the prior distribution which can be hard to justify and which introduce unnecessary tuning parameters. We extend the nonparametric maximum-likelihood estimator (NPMLE) for Gaussian location mixture densities to allow for multivariate,…

强化学习框架下的多变量动态中介分析AI 解读

Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there are multivariate and conditionally dependent mediators, and when the variables are observed over multiple time points. The problem is challenging, because the effect of a mediator involves not only the path from the treatment to this mediator itself at the current time point, but also all possible…

计算高效且统计最优的稳健高维线性回归AI 解读

High-dimensional linear regression under heavy-tailed noise or outlier corruption is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suffer from high computational costs, especially since the robust loss functions are usually nonsmooth. More recently, computationally fast nonconvex approaches via subgradient descent are proposed, which, unfortunately, fail to deliver a statistically consistent estimator even…

基于经验似然的多元正则变差检验AI 解读

Multivariate regular variation is a common assumption in the statistics literature and needs to be verified in real-data applications. We develop a novel hypothesis test for multivariate regular variation, employing localized empirical likelihood. We establish the weak convergence of the test statistic to a nonstandard, distribution-free limit and hence can provide universal critical values for the test. We show the very good finite-sample behavior of the procedure through…

大语言模型水印的统计框架：枢轴量、检测效率与最优规则AI 解读

Since ChatGPT was introduced in November 2022, embedding (nearly) unnoticeable statistical signals into text generated by large language models (LLMs), also known as watermarking, has been used as a principled approach to provable detection of LLM-generated text from its human-written counterpart. In this paper, we introduce a general and flexible framework for reasoning about the statistical efficiency of watermarks and designing powerful detection rules. Inspired by the…

非参数回归中的最优异方差检验AI 解读

Heteroskedasticity testing in nonparametric regression is a classic statistical problem with important practical applications, yet fundamental limits are unknown. Adopting a minimax perspective, this article considers the testing problem in the context of an α-Hölder mean and a β-Hölder variance function. For α>0 and β∈(0,1/2), the sharp minimax separation rate n−4α+n−4β/(4β+1)+n−2β is established. To achieve the minimax separation rate, a kernel-based statistic using…

格点上高维随机场的一般时空因子模型AI 解读

Motivated by the need for analysing large spatio-temporal panel data, we introduce a novel nonparametric methodology for n-dimensional random fields observed across S spatial locations and T time periods. We call it general spatio-temporal factor model (GSTFM). First, we provide the probabilistic and mathematical underpinning needed for the representation of a random field as the sum of two components: the common component (driven by a small number q of latent factors) and…

关于两个响应变量交互关系的有效降维理论AI 解读

Efficient dimension reduction regarding the interaction between two response variables, which facilitates statistical analysis in multiple important application scenarios, was initially discussed by Luo (J. R. Stat. Soc. Ser. B. Stat. Methodol. 84 (2022) 269–294). The efficient dimension reduction subspaces were introduced, and, under mild conditions on the predictor, they were equated with the family of dual inverse regression subspaces. Besides the general framework,…