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企业不确定性的调查测量AI 解读

We elicit subjective probability distributions from business executives about their own-firm outcomes at a one-year look-ahead horizon. In terms of question design, our key innovation is to let survey respondents freely select support points and probabilities in five-point distributions over future sales growth, employment, and investment. In terms of data collection, we develop and field a new monthly panel Survey of Business Uncertainty. The SBU began in 2014 and now…

激励驱动的注意力不足AI 解读

“Rational inattention” is becoming increasingly prominent in economic modeling, but there is little empirical evidence for its central premise-that the choice of attention results from a cost-benefit optimization. Observational data typically do not allow researchers to infer attention choices from observables. We fill this gap in the literature by exploiting a unique dataset of professional forecasters who update their inflation forecasts at days of their choice. In the…

金融研究中的计数与类计数数据AI 解读

This paper assesses different econometric approaches to working with count-based outcome variables and other outcomes with similar distributions, which are increasingly common in corporate finance applications. We demonstrate that the common practice of estimating linear regressions of the log of 1 plus the outcome produces estimates with no natural interpretation that can have the wrong sign in expectation. In contrast, a simple fixed-effects Poisson model produces…

让富人受淹：哈维飓风后的金融援助分配与财务困境AI 解读

Outside of flood hazard zones, households must decide whether to insure or rely on disaster assistance to manage flood risk. We use the quasi-random flooding generated by Hurricane Harvey, which hit Houston in August 2017, to understand the implications of flood losses for households with differing access to insurance and credit. Outside the floodplain, credit-constrained homeowners experience a 20% increase in bankruptcies and a 13% increase in the share of debt in severe…

利用高频数据预测低频宏观事件AI 解读

Summary High‐frequency financial and economic indicators are usually time‐aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed‐frequency alternative that delivers high‐frequency probability forecasts (including their confidence bands) for low‐frequency events. The new approach is compared with single‐frequency alternatives using loss functions for rare‐event forecasting. We find (i) the weekly‐sampled term spread improves over…

含高阶网络相互依赖的多元面板Probit贝叶斯估计：广东企业全球市场参与AI 解读

Summary This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross‐sectional units are observed over a short period of time and cross‐sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modeling the decay function in network neighborliness (e.g., by disentangling the importance of rings of neighbors) or towards…

长期预测能力检验比你想象的更糟AI 解读

Summary We derive asymptotic results for the long‐horizon ordinary least squares (OLS) estimator and corresponding ‐statistic for stationary autoregressive predictors. The ‐statistic—formed using the correct asymptotic variance—together with standard‐normal critical values result in a correctly‐sized test for exogenous predictors. For endogenous predictors, the test is size distorted regardless of the persistence in the predictor and adjusted critical values are necessary.…

正态，却有偏态？AI 解读

Summary We propose a multivariate normality test against skew normal distributions using higher‐order log‐likelihood derivatives, which is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. Numerically, it is the supremum of the univariate skewness coefficient test over all linear combinations of the variables. We can simulate its exact finite sample distribution for any multivariate dimension and sample size. Our Monte Carlo…

使用插补因变量的回归AI 解读

Summary Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching‐based imputation procedures lead to inconsistent estimates. We offer a consistent and easily implemented two‐step…

《使用 R 的半参数回归》AI 解读

Semiparametric Regression with R by Harezlak, Ruppert, and Wand is a truly heroic book! To many of us, statistics can be roughly dichotomized into parametric and nonparametric. Semiparametric stati...

函数型混合效应聚类及其在纵向泌尿系统慢性盆腔疼痛症状数据中的应用AI 解读

By clustering patients with the urologic chronic pelvic pain syndromes (UCPPS) into homogeneous subgroups and associating these subgroups with baseline covariates and other clinical outcomes, we provide opportunities to investigate different potential elements of pathogenesis, which may also guide us in selection of appropriate therapeutic targets. Motivated by the longitudinal urologic symptom data with extensive subject heterogeneity and differential variability of…

贝叶斯稀疏线性回归的神经元化先验AI 解读

Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the…

基于多受试者时间序列的多层偏相关推断AI 解读

Partial correlations are commonly used to analyze the conditional dependence among variables. In this work, we propose a hierarchical model to study both the subject- and population-level partial correlations based on multi-subject time-series data. Multiple testing procedures adaptive to temporally dependent data with false discovery proportion control are proposed to identify the nonzero partial correlations in both the subject and population levels. A computationally…

时变随机对象与动态网络的建模AI 解读

Samples of dynamic or time-varying networks and other random object data such as time-varying probability distributions are increasingly encountered in modern data analysis. Common methods for time-varying data such as functional data analysis are infeasible when observations are time courses of networks or other complex non-Euclidean random objects that are elements of general metric spaces. In such spaces, only pairwise distances between the data objects are available and…

高维时间序列的断点推断AI 解读

For multiple change-points detection of high-dimensional time series, we provide asymptotic theory concerning the consistency and the asymptotic distribution of the breakpoint statistics and estimated break sizes. The theory backs up a simple two-step procedure for detecting and estimating multiple change-points. The proposed two-step procedure involves the maximum of a MOSUM (moving sum) type statistics in the first step and a CUSUM (cumulative sum) refinement step on an…

有偏抽样下的独立性检验AI 解读

Testing for dependence between pairs of random variables is a fundamental problem in statistics. In some applications, data are subject to selection bias that can create spurious dependence. An important example is truncation models, in which observed pairs are restricted to a specific subset of the X-Y plane. Standard tests for independence are not suitable in such cases, and alternative tests that take the selection bias into account are required. Here, we generalize the…

分位数回归中的单指标门限模型AI 解读

Threshold regression models are useful for identifying subgroups with heterogeneous parameters. The conventional threshold regression models split the sample based on a single and observed threshold variable, which enforces the threshold point to be equal for all subgroups of the population. In this article, we consider a more flexible single-index threshold model in the quantile regression setup, in which the sample is split based on a linear combination of predictors. We…

多模态数据的整合因子回归及其推断AI 解读

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an…

非可微先验下贝叶斯推断的梯度型 MCMCAI 解读

The use of nondifferentiable priors in Bayesian statistics has become increasingly popular, in particular in Bayesian imaging analysis. Current state-of-the-art methods are approximate in the sense that they replace the posterior with a smooth approximation via Moreau-Yosida envelopes, and apply gradient-based discretized diffusions to sample from the resulting distribution. We characterize the error of the Moreau-Yosida approximation and propose a novel implementation using…

保护个体数据的高维异质研究整合回归AI 解读

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar…

半监督线性回归AI 解读

We study a regression problem where for some part of the data we observe both the label variable (Y) and the predictors (X), while for other part of the data only the predictors are given. Such a problem arises, for example, when observations of the label variable are costly and may require a skilled human agent. When the conditional expectation E[Y|X] is not exactly linear, one can consider the best linear approximation to the conditional expectation, which can be estimated…

聚类分区随机试验的设计型比率估计与中心极限定理AI 解读

This article develops design-based ratio estimators for clustered, blocked randomized controlled trials (RCTs), with an application to a federally funded, school-based RCT testing the effects of behavioral health interventions. We consider finite population weighted least-square estimators for average treatment effects (ATEs), allowing for general weighting schemes and covariates. We consider models with block-by-treatment status interactions as well as restricted models…

可交换结构化交互过程的分层网络模型AI 解读

Network data often arises via a series of structured interactions among a population of constituent elements. E-mail exchanges, for example, have a single sender followed by potentially multiple receivers. Scientific articles, on the other hand, may have multiple subject areas and multiple authors. We introduce a statistical model, termed the Pitman-Yor hierarchical vertex components model (PY-HVCM), that is well suited for structured interaction data. The proposed PY-HVCM…

数据集偏移下的广义贝叶斯类别比例学习AI 解读

Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of…