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Nowcasting tail risk to economic activity at a weekly frequency

以周频率即时预测经济活动的尾部风险

Summary This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making…

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Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters

时变波动下的稳健推断:专业预测者的实时评估

Summary In many forecast evaluation applications, standard tests as well as tests allowing for time‐variation in relative forecast ability build on heteroskedasticity‐and‐autocorrelation consistent (HAC) covariance estimators. Yet, the finite‐sample performance of these asymptotics is often poor. “Fixed‐ ” asymptotics, used to account for long‐run variance estimation, improve finite‐sample performance under homoskedasticity, but lose asymptotic pivotality under time‐varying…

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Optimal forecast under structural breaks

结构突变下的最优预测

Summary This paper develops an optimal combined estimator to forecast out‐of‐sample under structural breaks. When it comes to forecasting, using only the postbreak observations after the most recent break point may not be optimal. In this paper, we propose a new estimation method that exploits the prebreak information. In particular, we show how to combine the estimator using the full‐sample (i.e., both the prebreak and postbreak data) and the estimator using only the…

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Extremal connectedness of hedge funds

对冲基金的极端关联性

Abstract We propose a dynamic measure of extremal connectedness tailored to the short reporting period and unbalanced nature of hedge funds data. Using multivariate extreme value regression techniques, we estimate this measure conditional on factors reflecting the economic uncertainty and the state of the financial markets, and derive risk indicators reflecting the likelihood of extreme spillovers. Empirically, we study the dynamics of tail dependencies between hedge funds…

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Nonparametric Empirical Bayes Prediction

非参数经验贝叶斯预测

Henceforth, the Journal of the American Statistical Association 2022 Theory and Methods Invited Paper by Nikolaos Ignatiadis and Stefan Wager, “Nonparametric Empirical Bayes Analysis,” will be refe...

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Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”

对《非参数经验贝叶斯分析的置信区间》的讨论

We would like to start with congratulating the authors. Empirical Bayes estimation is a very old, well studied problem. However, construction of confidence intervals in empirical Bayes setting has ...

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Rejoinder: Confidence Intervals for Nonparametric Empirical Bayes Analysis

作者回复:非参数经验贝叶斯分析的置信区间

In our article, we introduced two general methods for conducting inference for empirical Bayes estimands. The discussants have contributed to our work by clarifying and raising important issues reg...

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Coverage Properties of Empirical Bayes Intervals: A Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis” by Ignatiadis and Wager

经验贝叶斯区间的覆盖性质:对非参数经验贝叶斯置信区间研究的讨论

Empirical Bayes methods are commonly used in the analysis of data coming from multiple related populations or groups, where often the goal is to obtain a parameter estimate for each individual grou...

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Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”

关于“非参数经验贝叶斯分析的置信区间”的讨论

We congratulate Ignatiadis and Wager (IW) on this significant step forward, incorporating uncertainty in the estimated prior into empirical Bayes (EB) analysis. EB analyses have traditionally ignor...

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Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis” by Ignatiadis and Wager

关于非参数经验贝叶斯分析置信区间的讨论

I congratulate the authors for a fantastic piece of work about confidence intervals in an inverse problem involving a mixing distribution under an empirical Bayes setup. We can regard the problem a...

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High-Dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity

高维中介分析:筛选连接童年创伤与皮质醇应激反应的 DNA 甲基化位点

Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a dataset consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of…

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Copula Regression for Compound Distributions with Endogenous Covariates with Applications in Insurance Deductible Pricing

含内生协变量的复合分布 Copula 回归及保险免赔额定价应用

This article concerns deductible pricing in nonlife insurance contracts. The primary interest of insurers is the effect of the contract deductible on a policyholder’s aggregate loss that is determined by a compound distribution where the sum of individual claim amount is stopped by the number of claims. Policyholders choose the deductible level based on their hidden risks, which makes deductible endogenous in the regressions for both claim frequency and claim severity. To…

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Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface

脑电脑机接口中神经活动的贝叶斯推断

A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian…

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Confidence Intervals for Nonparametric Empirical Bayes Analysis

非参数经验贝叶斯分析的置信区间

In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior. In this paper, we develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as…

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Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts

网络数据的动态随机区块回归模型:国际军事冲突应用

The decision to engage in military conflict is shaped by many factors, including state- and dyad-level characteristics as well as the state’s membership in geopolitical coalitions. Supporters of the democratic peace theory, for example, hypothesize that the community of democratic states is less likely to wage war with each other. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on…

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Semiparametric Multinomial Logistic Regression for Multivariate Point Pattern Data

多元点模式数据的半参数多项 logistic 回归

We propose a new method for analysis of multivariate point pattern data observed in a heterogeneous environment and with complex intensity functions. We suggest semiparametric models for the intensity functions that depend on an unspecified factor common to all types of points. This is for example well suited for analyzing spatial covariate effects on events such as street crime activities that occur in a complex urban environment. A multinomial conditional composite…

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Extremile Regression

极值均值回归(Extremile Regression)

Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the…

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High-Dimensional Spatial Quantile Function-on-Scalar Regression

高维空间分位数函数对标量回归

This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar…

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Nonparametric Tests of the Causal Null With Nondiscrete Exposures

非离散暴露下因果零假设的非参数检验

In many scientific studies, it is of interest to determine whether an exposure has a causal effect on an outcome. In observational studies, this is a challenging task due to the presence of confounding variables that affect both the exposure and the outcome. Many methods have been developed to test for the presence of a causal effect when all such confounding variables are observed and when the exposure of interest is discrete. In this article, we propose a class of…

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Survival Regression Models With Dependent Bayesian Nonparametric Priors

具有相依贝叶斯非参数先验的生存回归模型

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the…

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Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems

超高维监督学习特征筛选中的协变量信息数

Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensional supervised problems with sparse signals; that is, a limited number of observations (n), each with a very large number of covariates (p≫n), only a small share of which is truly associated with the response. In these settings, major concerns on computational burden, algorithmic stability, and statistical accuracy call for substantially reducing the feature space by eliminating…

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Minimax Efficient Random Experimental Design Strategies With Application to Model-Robust Design for Prediction

极小极大高效随机实验设计及其模型稳健预测应用

In game theory and statistical decision theory, a random (i.e., mixed) decision strategy often outperforms a deterministic strategy in minimax expected loss. As experimental design can be viewed as a game pitting the Statistician against Nature, the use of a random strategy to choose a design will often be beneficial. However, the topic of minimax-efficient random strategies for design selection is mostly unexplored, with consideration limited to Fisherian randomization of…

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Sensitivity Analysis via the Proportion of Unmeasured Confounding

基于未测量混杂比例的敏感性分析

In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby…

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Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design

无金标准病例定义下的疟疾疫苗效力估计:孟德尔析因设计

Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable…

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