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横截面预测表现的比较AI 解读

This paper develops new methods for pairwise comparisons of predictive accuracy with cross-sectional data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to test the null of equal predictive accuracy on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose…

动态条件特征值 GARCHAI 解读

In this paper we introduce a multivariate generalized autoregressive conditional heteroskedastic (GARCH) class of models with time-varying conditional eigenvalues. The dynamics of the eigenvalues is derived for the cases with nderlying Gaussian and Student’s t-distributed innovations based on the general theory of dynamic conditional score models by Creal, Koopman and Lucas (2013) and Harvey (2013). The resulting time-varying eigenvalue GARCH models – labeled ‘λ-GARCH’…

用 SVAR 与高维动态因子模型验证 DSGE 模型AI 解读

A popular validation procedure for Dynamic Stochastic General Equilibrium (DSGE) models consists in comparing the structural shocks and impulse-response functions obtained by estimation-calibration of the DSGE with those obtained in an Structural Vector Autoregressions (SVAR) identified by means of some of the DSGE restrictions. I show that this practice can be seriously misleading when the variables used in the SVAR contain measurement errors. If this is the case, for…

利用大型数据集测量产出缺口AI 解读

Abstract We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (comovements, nonstationarity, and the slow drift in long-run output growth over time). We find that (1) from the mid-1990s to 2008, the U.S. economy operated above its potential and (2) in 2018:Q4, the labor market was tighter than the market for goods…

存在有偏来源时基于汇总统计量的稳健融合与提取AI 解读

Summary Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption or model misspecification. Thus there is a need for robust data combination methods that can be used with biased sources. In this paper, a robust data fusion-extraction method is proposed. Unlike existing methods, the proposed method can be applied in the important case where researchers have no knowledge of…

基于高频金融数据的大型波动率矩阵自适应稳健估计AI 解读

Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behaviors, they require a sub-Gaussian or finite high-order moment assumption for observed log-returns, which cannot account for the heavy-tail phenomenon of stock-returns. Recently, a robust estimator was developed to handle heavy-tailed distributions with some bounded fourth-moment assumption.…

具有双向不可观测异质性的线性面板回归AI 解读

We study linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known functional form (additive or multiplicative). In this paper, we allow for this functional form to be more general and unknown. We discuss two different estimation approaches that allow consistent…

工具相关性时变下美国货币政策冲击的汇率效应与溢出识别AI 解读

Summary We propose a novel econometric approach to estimating time‐varying policy effects using external instruments in the presence of time‐varying instrument relevance in a factor‐augmented VAR model with data on the United States, Canada, Germany, Japan, and the United Kingdom. We find that US monetary policy shocks are an important driver of the exchange rate movements, with no delayed overshooting. We show that estimates of spillover effects of US monetary policy shocks…

大型电子健康记录分析：稀有事件下的最优 Cox 回归子抽样AI 解读

Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors, including a time-dependent coefficient, which transforms the dataset into “pseudo-observation” form, thus, critically inflating its size. A popular way for coping with…

利用提升式离散时间竞争风险模型分析老年患者术后死亡AI 解读

Elderly patients are at a high risk of suffering from postoperative death. Personalized strategies to improve their recovery after intervention are therefore urgently needed. A popular way to analyze postoperative mortality is to develop a prognostic model that incorporates risk factors measured at hospital admission, for example, comorbidities. When building such models, numerous issues must be addressed, including censoring and the presence of competing events (such as…

协变量引导的潜在交互模型：校正鸟类与植物互动预测的地理及分类偏差AI 解读

Reductions in natural habitats urge that we better understand species’ interconnection and how biological communities respond to environmental changes. However, ecological studies of species’ interactions are limited by their geographic and taxonomic focus which can distort our understanding of interaction dynamics. We focus on bird–plant interactions that refer to situations of potential fruit consumption and seed dispersal. We develop an approach for predicting species’…

合成似然贝叶斯推断：渐近性质与调整AI 解读

Implementing Bayesian inference is often computationally challenging in complex models, especially when calculating the likelihood is difficult. Synthetic likelihood is one approach for carrying out inference when the likelihood is intractable, but it is straightforward to simulate from the model. The method constructs an approximate likelihood by taking a vector summary statistic as being multivariate normal, with the unknown mean and covariance estimated by simulation.…

分而治之：大规模时间序列的分布式层级因子建模AI 解读

This paper proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method employs a multiple-fold dimension reduction procedure using Principal Component Analysis (PCA) and shows great promises for modeling large-scale data that cannot be stored nor analyzed by a single machine. Each computer at the basic level performs a PCA to extract common factors among the time…

快速黎曼优化下的广义低秩加稀疏张量估计AI 解读

We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating…

具有因子结构的大型面板的稳健估计AI 解读

This article studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain…

用于提取微生物组成的零膨胀逻辑正态多项模型AI 解读

High throughput sequencing data collected to study the microbiome provide information in the form of relative abundances and should be treated as compositions. Although many approaches including scaling and rarefaction have been proposed for converting raw count data into microbial compositions, most of these methods simply return zero values for zero counts. However, zeros can distort downstream analyses, and they can also pose problems for composition-aware methods. This…

含发散尖峰的大维样本协方差矩阵线性谱统计量的中心极限定理AI 解读

In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSSs) of a large-dimensional sample covariance matrix when the population covariance matrices are involved with diverging spikes. This constitutes a nontrivial extension of the Bai–Silverstein theorem (BST) (Ann. Probab. 32 (2004) 553–605), a theorem that has strongly influenced the development of high-dimensional statistics, especially in the applications of random matrix theory to…

摘要暂缺

经典因子分析中的 Varimax 旋转具有统计推断作用AI 解读

Abstract In the 1930s, Psychologists began developing Multiple-Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In this form of factor analysis, the Varimax factor rotation redraws the axes through the multi-dimensional factors to make them sparse and thus make them more interpretable. Charles Spearman and many others objected to factor rotations because the factors seem to be…

归一化潜在测度因子模型AI 解读

Abstract We propose a methodology for modelling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalised random measures, we consider a prior distribution for a collection of discrete random measures where each measure is a linear combination of a set of latent measures, interpretable as characteristic traits shared by different distributions, with positive random weights. The model is nonidentified and a method for…

Ying Zhou与Xinyi Zhang对Varimax因子分析论文的讨论AI 解读

We congratulate Rohe and Zeng for their inspiring work on providing theoretical support for Varimax rotation in factor analysis. A key contribution of the article is the demonstration that if the factors in the semi-parametric factor model follow heavy-tailed distribution, then performing principal components analysis (PCA) on the observed data matrix along with Varimax rotation applied to the principal components does produce interpretable explanatory variables. We have the…

Van Werde对《Varimax经典因子分析可进行统计推断》的讨论AI 解读

I would like to point out that the author’s results have potential applications in the theory of random graphs beyond those for block models which are explicitly remarked upon in the paper. In particular, the results could be used to resolve the nonidentifiability associated with random dot product graphs (Athreya et al., 2017, Remark 1). The authors are most likely already aware of this connection: they have previously worked on generalized random dot product graphs in Rohe…

We congratulate Rohe and Zeng on their interesting paper and would like to share two comments and propose one question for their consideration. First, it is impressive to see the large variety of models addressed by the ‘vintage sparse PCA’ (vsp) framework, ranging from traditional factor models to explainable and sparse PCA but also including topic models and stochastic block models. In our view, the demonstration of the broad applicability of the ‘compress and rotate’…

Pargent、Goretzko 与 von Oertzen 对《经典因子分析结合 Varimax 可进行统计推断》的讨论AI 解读

As psychologists, we appreciate Rohe & Zeng’s (R&Z; Rohe & Zeng, 2022) new insights into ‘vintage’ principal component analysis with varimax rotation (PCA+VR). Theories of intelligence and personality, perhaps psychology’s contributions best known outside of our field, have been a direct product of PCA. PCA+VR is still widely used for developing and evaluating psychological tests and questionnaires, although the literature has fought against it in favour of more complex…