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目标主成分分析：高维面板数据的迁移学习AI 解读

This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not…

早期儿童干预收益分布的分位数AI 解读

Summary We investigate the distribution of gains among participants in the Infant Health and Development Program, an understudied randomized controlled trial that targets infants with low birth weight. Our primary focus is on assessing the effects in cognitive and health outcomes within distinct subgroups, which we define based on the outcomes that would occur in the absence of program participation. We propose a strategy to estimate the distribution of gains from the…

潜在双向因子结构矩阵时间序列的在线变点检测AI 解读

This paper proposes a novel methodology for the online detection of changepoints in the factor structure of large matrix time series. Our approach is based on the well-known fact that, in the presence of a changepoint, the number of spiked eigenvalues in the second moment matrix of the data increases (e.g., in the presence of a change in the loadings, or if a new factor emerges). Based on this, we propose two families of procedures—one based on the fluctuations of partial…

高维面板数据模型的 GMM 估计AI 解读

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel…

因子增广回归中稀疏特异成分的检验AI 解读

We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative model augmented with sparse idiosyncratic components. The asymptotic properties of the test are established under time series dependence and polynomial tails. We outline a data-driven rule to select the tuning parameter and prove its theoretical validity. In simulation experiments, our procedure exhibits high power against sparse alternatives and low power…

含动态潜在因子的小维度 VAR 模型中的工具变量推断AI 解读

We study semiparametric inference in a small-dimensional vector autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q . This state-space specification is useful to measure separately the direct causality effects and the responses to dynamic common factors. We show that the state-space parameters are identifiable from the autocovariance function of the observed process. We estimate the model by…

应对意外：经济增长的压力情景AI 解读

Summary We propose the construction of conditional growth densities under stressed factor scenarios to assess the level of exposure of an economy to small probability but potentially catastrophic economic and/or financial scenarios, which can be either domestic or international. The choice of severe yet plausible stress scenarios is based on the joint probability distribution of the underlying factors driving growth, which are extracted with a multilevel dynamic factor model…

全球经济疲弱程度的实时测度AI 解读

Summary We propose an empirical framework to measure the real‐time weakness of the global economy. This framework relies on nonlinear factor models to identify recessionary and expansionary episodes, fitted to several macroeconomic variables, for the largest advanced and emerging economies. The country‐specific inferences are then combined to construct both a Global Weakness Index and a Global Intensity Index. As new economic data become available from different regions,…

通过迭代投影奇异值分解进行半参数张量因子分析AI 解读

Abstract This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend tensor factor models by incorporating auxiliary covariates in the loading matrices. We propose an algorithm of iteratively projected singular value decomposition (IP-SVD) for the semi-parametric estimation. It iteratively…

电子健康记录中的树引导稀有特征选择与逻辑聚合AI 解读

Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer…

基于因子调整向量自回归的高维时间序列分段AI 解读

Vector autoregressive (VAR) models are popularly adopted for modelling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modelling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose…

利用潜在多中介路径消除因果推断中的混杂AI 解读

Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates…

因子结构结果研究中对未观测混杂的敏感性分析AI 解读

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and…

FNETS：高维时间序列的因子调整网络估计与预测AI 解读

–We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three steps: (i)…

收入动态与技能的代际传递AI 解读

This paper develops and estimates a two-factor model of intergenerational skill transmission when earnings inequality reflects differences in individual skills and other non-skill shocks. We consider heterogeneity in both initial skills and skill growth rates, allowing variation in skill growth to change over the lifecycle. Using administrative tax data on two linked generations of Canadians covering 37 years, we exploit covariances in log earnings (at different ages) both…

多因子资产定价模型的样本内与样本外夏普比率AI 解读

Using available return data, many multi-factor asset pricing models present impressive in-sample Sharpe ratios, significantly surpassing that of the market portfolio. Such a performance, however, contradicts the conventional wisdom in finance. Investors cannot realistically attain the in-sample Sharpe ratios. They obtain the out-of-sample Sharpe ratios, which are significantly lower. Estimation risk is one reason for this performance deterioration. We theoretically study the…

稠密因子增强模型下的岭回归AI 解读

This article establishes a comprehensive theory of the optimality, robustness, and cross-validation selection consistency for the ridge regression under factor-augmented models with possibly dense idiosyncratic information.Using spectral analysis for random matrices, we show that the ridge regression is asymptotically efficient in capturing both factor and idiosyncratic information by minimizing the limiting predictive loss among the entire class of spectral regularized…

用于高维时间序列聚类的因子建模AI 解读

We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure.Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned.Our setting also offers the flexibility that some time series may not belong to any clusters.The consistency with explicit convergence rates is established for the estimation of the common factors, the…

利用潜在因子模型子群结构的众包标注AI 解读

Crowdsourcing has emerged as an alternative solution for collecting large scale labels. However, the majority of recruited workers are not domain experts, so their contributed labels could be noisy. In this paper, we propose a two-stage model to predict the true labels for multicategory classification tasks in crowdsourcing. In the first stage, we fit the observed labels with a latent factor model and incorporate subgroup structures for both tasks and workers through a…

潜在因子回归与稀疏回归是否充分？AI 解读

We propose the Factor Augmented sparse linear Regression Model (FARM) that not only encompasses both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression together. We provide theoretical guarantees for the estimation of our model under the existence of sub-Gaussian and heavy-tailed noises (with bounded (1+x)-th moment, for all x>0), respectively. In addition, the existing works on supervised…

交互效应面板中解释变量与因子载荷条件独立性的 LM 检验AI 解读

A huge literature on modeling cross-sectional dependence in panels has been developed using interactive effects (IE).One area of contention is the hypothesis concerned with whether the regressors and factor loadings are correlated or not.Under the null hypothesis that they are conditionally independent, we can still apply the consistent and robust two-way fixed effects estimator.As an important specification test we develop an LM test for both static and dynamic panels with…

合成控制方法的设计视角AI 解读

Since their introduction by Abadie and Gardeazabal, Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observational studies in settings with panel data.Formal discussions often motivate SC methods by the assumption that the potential outcomes were generated by a factor model.Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s).We show that…

基于条件分位数变分自编码器的资产定价AI 解读

We propose a new asset pricing model that is applicable to the big panel of return data. The main idea of this model is to learn the conditional distribution of the return, which is approximated by a step distribution function constructed from conditional quantiles of the return. To study conditional quantiles of the return, we propose a new conditional quantile variational autoencoder (CQVAE) network. The CQVAE network specifies a factor structure for conditional quantiles…

高维因子定价模型中 alpha 的自适应检验AI 解读

This article proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of assets. Because the market’s inefficient pricing is likely to occur to a small fraction of exceptional assets, we develop a testing procedure that is particularly powerful against sparse signals. Based on the high-dimensional Gaussian approximation theory, we propose a simulation-based approach to…