Journal of Business & Economic Statistics最新文献_第8页

Covariate-Assisted Community Detection in Multi-Layer Networks 多层网络中的协变量辅助社区检测

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-06-02 DOI: 10.1080/07350015.2022.2085726

Shi Xu, Yao Zhen, Junhui Wang

引用次数: 7

Detection of Multiple Structural Breaks in Large Covariance Matrices 大协方差矩阵中多个结构断裂的检测

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-05-18 DOI: 10.1080/07350015.2022.2076686

Yu-Ning Li, Degui Li, P. Fryzlewicz

{"title":"Detection of Multiple Structural Breaks in Large Covariance Matrices","authors":"Yu-Ning Li, Degui Li, P. Fryzlewicz","doi":"10.1080/07350015.2022.2076686","DOIUrl":"https://doi.org/10.1080/07350015.2022.2076686","url":null,"abstract":"ABSTRACT This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An package “ ” is provided to implement the proposed algorithms.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45166385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data 大型面板数据中具有交互效应的线性模型中的异方差、误差序列相关和斜率异质性的稳健方法

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-05-13 DOI: 10.1080/07350015.2022.2077349

Guowei Cui, Kazuhiko Hayakawa, Shuichi Nagata, Takashi Yamagata

{"title":"A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data","authors":"Guowei Cui, Kazuhiko Hayakawa, Shuichi Nagata, Takashi Yamagata","doi":"10.1080/07350015.2022.2077349","DOIUrl":"https://doi.org/10.1080/07350015.2022.2077349","url":null,"abstract":"Abstract In this article, we propose a robust approach against heteroscedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data. First, consistency and asymptotic normality of the pooled iterated principal component (IPC) estimator for random coefficient and homogeneous slope models are established. Then, we prove the asymptotic validity of the associated Wald test for slope parameter restrictions based on the panel heteroscedasticity and autocorrelation consistent (PHAC) variance matrix estimator for both random coefficient and homogeneous slope models, which does not require the Newey-West type time-series parameter truncation. These results asymptotically justify the use of the same pooled IPC estimator and the PHAC standard error for both homogeneous-slope and heterogeneous-slope models. This robust approach can significantly reduce the model selection uncertainty for applied researchers. In addition, we propose a Lagrange Multiplier (LM) test for correlated random coefficients with covariates. This test has nontrivial power against correlated random coefficients, but not for random coefficients and homogeneous slopes. The LM test is important because the IPC estimator becomes inconsistent with correlated random coefficients. The finite sample evidence and an empirical application support the reliability and the usefulness of our robust approach.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59995703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices 已实现协方差矩阵的奇异条件自回归Wishart模型

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-05-11 DOI: 10.1080/07350015.2022.2075370

Gustav Alfelt, Taras Bodnar, F. Javed, J. Tyrcha

{"title":"Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices","authors":"Gustav Alfelt, Taras Bodnar, F. Javed, J. Tyrcha","doi":"10.1080/07350015.2022.2075370","DOIUrl":"https://doi.org/10.1080/07350015.2022.2075370","url":null,"abstract":"Abstract Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sector wise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20-year long time series containing 50 stocks and to a 10-year long time series containing 300 stocks, and evaluated using out-of-sample forecast accuracy. It outperforms the benchmark models with high statistical significance and the parsimonious specifications perform better than the baseline SCAW model, while using considerably less parameters.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42036077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Network Gradient Descent Algorithm for Decentralized Federated Learning 分散联合学习的网络梯度下降算法

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-05-06 DOI: 10.1080/07350015.2022.2074426

Shuyuan Wu, Danyang Huang, Hansheng Wang

{"title":"Network Gradient Descent Algorithm for Decentralized Federated Learning","authors":"Shuyuan Wu, Danyang Huang, Hansheng Wang","doi":"10.1080/07350015.2022.2074426","DOIUrl":"https://doi.org/10.1080/07350015.2022.2074426","url":null,"abstract":"Abstract We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed network structure without a central master. This greatly enhances the reliability of the entire algorithm. Those nice properties inspire us to carefully study the NGD method both theoretically and numerically. Theoretically, we start with a classical linear regression model. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator’s statistical efficiency. The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is weakly balanced, even if the data are distributed heterogeneously. Those interesting findings are then extended to general models and loss functions. Extensive numerical studies are presented to corroborate our theoretical findings. Classical deep learning models are also presented for illustration purpose.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45116396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Estimation of Panel Data Models with Random Interactive Effects and Multiple Structural Breaks when T is Fixed T固定时具有随机交互效应和多个结构断裂的面板数据模型的估计

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-04-22 DOI: 10.1080/07350015.2022.2067546

Y. Kaddoura, J. Westerlund

引用次数: 4

Combining p-values for Multivariate Predictive Ability Testing 组合p值进行多元预测能力测试

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-04-19 DOI: 10.1080/07350015.2022.2067545

Lars Spreng, G. Urga

引用次数: 0

Structural Breaks in Grouped Heterogeneity 群体异质性中的结构断裂

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-04-08 DOI: 10.1080/07350015.2022.2063132

Simon C. Smith

引用次数: 4

Rejoinder: “Co-citation and Co-authorship Networks of Statisticians” 复辩状：“统计学家的共同引用和合作网络”

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-04-03 DOI: 10.1080/07350015.2022.2055358

Pengsheng Ji, Jiashun Jin, Z. Ke, Wanshan Li

{"title":"Rejoinder: “Co-citation and Co-authorship Networks of Statisticians”","authors":"Pengsheng Ji, Jiashun Jin, Z. Ke, Wanshan Li","doi":"10.1080/07350015.2022.2055358","DOIUrl":"https://doi.org/10.1080/07350015.2022.2055358","url":null,"abstract":"We thank David Donoho for very encouraging comments. As always, his penetrating vision and deep thoughts are extremely stimulating. We are glad that he summarizes a major philosophical difference between statistics in earlier years (e.g., the time of Francis Galton) and statistics in our time by just a few words: data-first versus model-first. We completely agree with his comment that “each effort by a statistics researcher to understand a newly available type of data enlarges our field; it should be a primary part of the career of statisticians to cultivate an interest in cultivating new types of datasets, so that new methodology can be discovered and developed”; these are exactly the motivations underlying our (several-year) efforts in collecting, cleaning, and analyzing a large-scale high-quality dataset. We would like to add that both traditions have strengths, and combining the strengths of two sides may greatly help statisticians deal with the so-called crisis of the 21st century in statistics we face today. Let us explain the crisis above first. In the model-first tradition, with a particular application problem in mind, we propose a model, develop a method and justify its optimality by some hard-to-prove theorems, and find a dataset to support the approach. In this tradition, we put a lot of faith on our model and our theory: we hope the model is adequate, and we hope our optimality theory warrants the superiority of our method over others. Modern machine learning literature (especially the recent development of deep learning) provides a different approach to justifying the “superiority” of an approach; we compare the proposed approach with existing approaches by the real data results over a dozen of benchmark datasets. To choose an algorithm for their dataset, a practitioner does not necessarily need warranties from a theorem; a superior performance over many benchmark datasets says it all. To some theoretical statisticians, this is rather disappointing, as they come from a long","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41393404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Discussion of “Cocitation and Coauthorship Networks of Statisticians” 关于“统计学家合著网络”的探讨

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-04-03 DOI: 10.1080/07350015.2022.2037432

Haolei Weng, Yang Feng

引用次数: 1