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高维因子模型中多个结构断点的分类与估计AI 解读

This paper proposes a quasi-maximum likelihood (QML) estimator for break points in high-dimensional factor models, specifically accounting for multiple structural breaks. We begin by establishing a necessary and sufficient condition to categorize two distinct types of breaks in factor loadings: singular changes and rotational changes. The analysis of the nearly singular subsample covariance matrices of the pseudo-factors plays a key role in our approach. It allows us to…

因子模型中结构脉冲响应的全局识别、估计与推断：统一框架AI 解读

This paper develops a theory for the global identification, estimation and inference of impulse response functions (IRFs) in structural factor models (SFMs). We examine the impact of normalization choices on IRF identification and propose to use identification restrictions robust to such choices. A new theorem is established to address IRF identification under both recursive and nonrecursive schemes in SFMs. Moreover, we develop two new estimators for structural IRFs under…

重刊：因子模型结构变化的似然比检验AI 解读

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break…

因子模型结构变化的似然比检验AI 解读

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break…

有技能共同基金的筛选：依赖情形下的错误发现控制AI 解读

Selecting skilled mutual funds through the multiple testing framework has received increasing attention from finance researchers and statisticians. The intercept α of Carhart four-factor model is commonly used to measure the true performance of mutual funds, and positive α’s are considered as skilled. We observe that the standardized ordinary least-square estimates of α’s across the funds possess strong dependence and nonnormality structures, indicating that the conventional…

高维因子模型突变点的拟极大似然估计AI 解读

This paper proposes a quasi-maximum likelihood (QML) estimator of the break point for large-dimensional factor models with a single structural break in the factor loading matrix. We show that the QML estimator is consistent for the true break point when the covariance matrix of the pre- or post-break factor loading (or both) is singular. Consistency here means that the deviation of the estimated break date from the true break date k0 converges to zero as the sample size…

对潜在非平稳性稳健的结构 VAR 工具变量估计AI 解读

This paper considers the estimation of dynamic causal effects using a proxy structural vector-autoregressive model with possibly nonstationary regressors. We provide general conditions under which the asymptotic normal approximation remains valid. In this case, the asymptotic variance depends on the persistence property of each series. We further provide a consistent asymptotic covariance matrix estimator that requires neither knowledge of the presistence properties of the…

含全局因子与组特定因子的因子模型收缩估计AI 解读

This article develops an adaptive group lasso estimator for factor models with both global and group-specific factors. The global factors can affect all variables, whereas the group-specific factors are only allowed to affect the variables within a certain group. We propose a new method to separately identify the spaces spanned by global and group-specific factors, and we develop a new shrinkage estimator that can consistently estimate the factor loadings and determine the…

高维因子模型中变点的估计与推断AI 解读

In this paper, we consider the estimation of break points in high-dimensional factor models where the unobserved factors are estimated by principal component analysis (PCA). The factor loading matrix is assumed to have a structural break at an unknown time. We establish the conditions under which the least squares (LS) estimator is consistent for the break date. Our consistency result holds for both large and small breaks. We also find the LS estimator’s asymptotic…

超高维稀疏数据中基于条件严格凸损失的非参数筛选AI 解读

Sure screening technique has been considered as a powerful tool to handle the ultrahigh dimensional variable selection problems, where the dimensionality $p$ and the sample size $n$ can satisfy the NP dimensionality $\log p=O(n^{a})$ for some $a>0$ [J. R. Stat. Soc. Ser. B. Stat. Methodol. 70 (2008) 849–911]. The current paper aims to simultaneously tackle the “universality” and “effectiveness” of sure screening procedures. For the “universality,” we develop a general and…

具有过度识别限制的动态结构因子模型估计与推断AI 解读

This paper develops a new estimator for the impulse response functions in structural factor models with a fixed number of over-identifying restrictions. The proposed identification scheme nests the conventional just-identified recursive scheme as a special case. We establish the asymptotic distributions of the new estimator and develop test statistics for the over-identifying restrictions. Simulation results show that adding a few more over-identifying restrictions can lead…

多无效矩条件下的自适应弹性网GMM估计：模型与矩条件联合选择AI 解读

This article develops the adaptive elastic net generalized method of moments (GMM) estimator in large-dimensional models with potentially (locally) invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the adaptively weighted lasso shrinkage and the quadratic regularization. It is a one-step…

未知依赖结构下的错误发现比例估计AI 解读

Summary Large-scale multiple testing with correlated test statistics arises frequently in much scientific research. Incorporating correlation information in approximating the false discovery proportion (FDP) has attracted increasing attention in recent years. When the covariance matrix of test statistics is known, Fan and his colleagues provided an accurate approximation of the FDP under arbitrary dependence structure and some sparsity assumption. However, the covariance…

动态因子模型中的参数不稳定性检验AI 解读

In this paper, we develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, conventional tests cannot be used. Based on the fact that the presence of a structural change in factor loadings yields a structural change in second moments of factors obtained from the full sample…

因子增强向量自回归模型的过度识别约束检验AI 解读

This paper develops tests for overidentifying restrictions in Factor-Augmented Vector Autoregressive (FAVAR) models. The identification of structural shocks in FAVAR can involve infinitely many restrictions as the number of cross sections goes to infinity. Our focus is to test the joint null hypothesis that all the restrictions are satisfied. Conventional tests cannot be used due to the large dimension. We transform the infinite-dimensional problem into a finite-dimensional…

使用组桥估计量选择近似因子模型的正确因子数：大面板情形AI 解读

This article proposes a group bridge estimator to select the correct number of factors in approximate factor models. It contributes to the literature on shrinkage estimation and factor models by extending the conventional bridge estimator from a single equation to a large panel context. The proposed estimator can consistently estimate the factor loadings of relevant factors and shrink the loadings of irrelevant factors to zero with a probability approaching one. Hence, it…

任意协方差依赖下的错误发现比例估计AI 解读

Multiple hypothesis testing is a fundamental problem in high dimensional inference, with wide applications in many scientific fields. In genome-wide association studies, tens of thousands of tests are performed simultaneously to find if any SNPs are associated with some traits and those tests are correlated. When test statistics are correlated, false discovery control becomes very challenging under arbitrary dependence. In the current paper, we propose a novel method based…