Journal of Business & Economic Statistics最新文献_第7页

Specification Testing of Regression Models with Mixed Discrete and Continuous Predictors 离散和连续混合预测回归模型的规格检验

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110879

Xuehu Zhu, Qiming Zhang, Lixing Zhu, Jun Zhang, Luoyao Yu

{"title":"Specification Testing of Regression Models with Mixed Discrete and Continuous Predictors","authors":"Xuehu Zhu, Qiming Zhang, Lixing Zhu, Jun Zhang, Luoyao Yu","doi":"10.1080/07350015.2022.2110879","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110879","url":null,"abstract":"Abstract This article proposes a nonparametric projection-based adaptive-to-model specification test for regressions with discrete and continuous predictors. The test statistic is asymptotically normal under the null hypothesis and omnibus against alternative hypotheses. The test behaves like a locally smoothing test as if the number of continuous predictors was one and can detect the local alternative hypotheses distinct from the null hypothesis at the rate that can be achieved by existing locally smoothing tests for regressions with only one continuous predictor. Because of the model adaptation property, the test can fully use the model structure under the null hypothesis so that the dimensionality problem can be significantly alleviated. A discretization-expectation ordinary least squares estimation approach for partial central subspace in sufficient dimension reduction is developed as a by-product in the test construction. We suggest a residual-based wild bootstrap method to give an approximation by fully using the null model and thus closer to the limiting null distribution than existing bootstrap approximations. We conduct simulation studies to compare it with existing tests and two real data examples for illustration.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1101 - 1115"},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48057158","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

LASSO for Stochastic Frontier Models with Many Efficient Firms 具有多个有效企业的随机前沿模型的LASSO

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110881

W. Horrace, Hyunseok Jung, Yoonseok Lee

引用次数: 3

Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs 多输入和多输出的非参数随机前沿模型

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110882

L. Simar, P. W. Wilson

{"title":"Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs","authors":"L. Simar, P. W. Wilson","doi":"10.1080/07350015.2022.2110882","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110882","url":null,"abstract":"Abstract Stochastic frontier models along the lines of Aigner et al. are widely used to benchmark firms’ performances in terms of efficiency. The models are typically fully parametric, with functional form specifications for the frontier as well as both the noise and the inefficiency processes. Studies such as Kumbhakar et al. have attempted to relax some of the restrictions in parametric models, but so far all such approaches are limited to a univariate response variable. Some (e.g., Simar and Zelenyuk; Kuosmanen and Johnson) have proposed nonparametric estimation of directional distance functions to handle multiple inputs and outputs, raising issues of endogeneity that are either ignored or addressed by imposing restrictive and implausible assumptions. This article extends nonparametric methods developed by Simar et al. and Hafner et al. to allow multiple inputs and outputs in an almost fully nonparametric framework while avoiding endogeneity problems. We discuss properties of the resulting estimators, and examine their finite-sample performance through Monte Carlo experiments. Practical implementation of the method is illustrated using data on U.S. commercial banks.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1391 - 1403"},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48001020","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: 6

Bagged Pretested Portfolio Selection 袋装预测试投资组合选择

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110880

Ekaterina Kazak, W. Pohlmeier

{"title":"Bagged Pretested Portfolio Selection","authors":"Ekaterina Kazak, W. Pohlmeier","doi":"10.1080/07350015.2022.2110880","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110880","url":null,"abstract":"Abstract This article exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1116 - 1131"},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41418356","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: 0

Identification of SVAR Models by Combining Sign Restrictions With External Instruments 符号约束与外部仪器相结合的SVAR模型识别

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-07-29 DOI: 10.1080/07350015.2022.2104857

R. Braun, R. Brüggemann

引用次数: 16

Trends in Earnings Volatility Using Linked Administrative and Survey Data 使用关联管理和调查数据的收益波动趋势

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-07-15 DOI: 10.1080/07350015.2022.2102023

James P. Ziliak, Charles M. Hokayem, C. Bollinger

引用次数: 9

Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity 具有未观测异质性分组模式的纵向数据的大规模广义线性模型

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-07-06 DOI: 10.1080/07350015.2022.2097913

T. Ando, Jushan Bai

引用次数: 0

Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates 具有组合协变量的高维线性对数对比度模型的鲁棒信号恢复

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-07-06 DOI: 10.1080/07350015.2022.2097911

Dongxiao Han, Jian Huang, Yuanyuan Lin, Lei Liu, Lianqiang Qu, Liuquan Sun

引用次数: 1

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":"41 1","pages":"846 - 861"},"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