Journal of Business & Economic Statistics最新文献_第5页

Nonparametric prediction distribution from resolution-wise regression with heterogeneous data. 基于异构数据的分辨率明智回归的非参数预测分布

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2023-01-01 Epub Date: 2022-10-06 DOI: 10.1080/07350015.2022.2115498

Jialu Li, Wan Zhang, Peiyao Wang, Qizhai Li, Kai Zhang, Yufeng Liu

{"title":"Nonparametric prediction distribution from resolution-wise regression with heterogeneous data.","authors":"Jialu Li, Wan Zhang, Peiyao Wang, Qizhai Li, Kai Zhang, Yufeng Liu","doi":"10.1080/07350015.2022.2115498","DOIUrl":"10.1080/07350015.2022.2115498","url":null,"abstract":"Modeling and inference for heterogeneous data have gained great interest recently due to rapid developments in personalized marketing. Most existing regression approaches are based on the conditional mean and may require additional cluster information to accommodate data heterogeneity. In this paper, we propose a novel nonparametric resolution-wise regression procedure to provide an estimated distribution of the response instead of one single value. We achieve this by decomposing the information of the response and the predictors into resolutions and patterns respectively based on marginal binary expansions. The relationships between resolutions and patterns are modeled by penalized logistic regressions. Combining the resolution-wise prediction, we deliver a histogram of the conditional response to approximate the distribution. Moreover, we show a sure independence screening property and the consistency of the proposed method for growing dimensions. Simulations and a real estate valuation dataset further illustrate the effectiveness of the proposed method.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1157-1172"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47877556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Male Earnings Volatility in LEHD before, during, and after the Great Recession. 大衰退之前、期间和之后 LEHD 的男性收入波动性。

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2023-01-01 Epub Date: 2022-10-27 DOI: 10.1080/07350015.2022.2126479

Kevin L McKinney, John M Abowd

引用次数: 6

Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis. 高维成分数据的稳健协方差矩阵估计及其在销售数据分析中的应用

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2023-01-01 Epub Date: 2022-09-21 DOI: 10.1080/07350015.2022.2106990

Danning Li, Arun Srinivasan, Qian Chen, Lingzhou Xue

{"title":"Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis.","authors":"Danning Li, Arun Srinivasan, Qian Chen, Lingzhou Xue","doi":"10.1080/07350015.2022.2106990","DOIUrl":"10.1080/07350015.2022.2106990","url":null,"abstract":"Compositional data arises in a wide variety of research areas when some form of standardization and composition is necessary. Estimating covariance matrices is of fundamental importance for high-dimensional compositional data analysis. However, existing methods require the restrictive Gaussian or sub-Gaussian assumption, which may not hold in practice. We propose a robust composition adjusted thresholding covariance procedure based on Huber-type M-estimation to estimate the sparse covariance structure of high-dimensional compositional data. We introduce a cross-validation procedure to choose the tuning parameters of the proposed method. Theoretically, by assuming a bounded fourth moment condition, we obtain the rates of convergence and signal recovery property for the proposed method and provide the theoretical guarantees for the cross-validation procedure under the high-dimensional setting. Numerically, we demonstrate the effectiveness of the proposed method in simulation studies and also a real application to sales data analysis.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1090-1100"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10730115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43434903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Time-Varying Network for Cryptocurrencies 加密货币的时变网络

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-11-11 DOI: 10.1080/07350015.2022.2146695

Li Guo, Wolfgang Karl Härdle, Yubo Tao

引用次数: 0

From Conditional Quantile Regression to Marginal Quantile Estimation with Applications to Missing Data and Causal Inference 从条件分位数回归到边际分位数估计及其在缺失数据和因果推理中的应用

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-10-26 DOI: 10.1080/07350015.2022.2140158

Huijuan Ma, J. Qin, Yong Zhou

{"title":"From Conditional Quantile Regression to Marginal Quantile Estimation with Applications to Missing Data and Causal Inference","authors":"Huijuan Ma, J. Qin, Yong Zhou","doi":"10.1080/07350015.2022.2140158","DOIUrl":"https://doi.org/10.1080/07350015.2022.2140158","url":null,"abstract":"Abstract It is well known that information on the conditional distribution of an outcome variable given covariates can be used to obtain an enhanced estimate of the marginal outcome distribution. This can be done easily by integrating out the marginal covariate distribution from the conditional outcome distribution. However, to date, no analogy has been established between marginal quantile and conditional quantile regression. This article provides a link between them. We propose two novel marginal quantile and marginal mean estimation approaches through conditional quantile regression when some of the outcomes are missing at random. The first of these approaches is free from the need to choose a propensity score. The second is double robust to model misspecification: it is consistent if either the conditional quantile regression model is correctly specified or the missing mechanism of outcome is correctly specified. Consistency and asymptotic normality of the two estimators are established, and the second double robust estimator achieves the semiparametric efficiency bound. Extensive simulation studies are performed to demonstrate the utility of the proposed approaches. An application to causal inference is introduced. For illustration, we apply the proposed methods to a job training program dataset.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1377 - 1390"},"PeriodicalIF":3.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42327144","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

Spectral Estimation of Large Stochastic Blockmodels with Discrete Nodal Covariates 离散节点协变量大随机块模型的谱估计

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-10-26 DOI: 10.1080/07350015.2022.2139709

A. Mele, Lingxin Hao, J. Cape, C. Priebe

引用次数: 3

Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks 通过Leptokurtic经济冲击识别结构向量自回归

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-10-13 DOI: 10.1080/07350015.2022.2134872

Markku Lanne, Keyan Liu, Jani Luoto

引用次数: 5

Narrative Restrictions and Proxies: Rejoinder 叙述限制和代理：复辩状

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-10-02 DOI: 10.1080/07350015.2022.2115710

R. Giacomini, T. Kitagawa, Matthew Read

引用次数: 1

Comments on “Narrative Restrictions and Proxies” by Giacomini, Kitagawa, and Read Giacomini、Kitagawa和Read的“叙事限制与代理”评论

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-10-02 DOI: 10.1080/07350015.2022.2102021

J. Rubio-Ramirez

引用次数: 0

Discussion of “Narrative Restrictions and Proxies” by Raffaella Giacomini, Toru Kitagawa, and Matthew Read 论贾科米尼、北川彻、里德的“叙事限制与代理”

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-10-02 DOI: 10.1080/07350015.2022.2096042

Mikkel Plagborg-Møller

{"title":"Discussion of “Narrative Restrictions and Proxies” by Raffaella Giacomini, Toru Kitagawa, and Matthew Read","authors":"Mikkel Plagborg-Møller","doi":"10.1080/07350015.2022.2096042","DOIUrl":"https://doi.org/10.1080/07350015.2022.2096042","url":null,"abstract":"I am grateful for the chance to discuss this characteristically insightful paper by Giacomini, Kitagawa, and Read (hence-forth GKR). Since the seminal contribution of Antolín-Díaz and Rubio-Ramírez (2018), narrative restrictions have rapidly become one of the go-to tools for sharpening causal inference in SVAR analysis. Giacomini, Kitagawa, and Read (2021) con-tributed greatly to our understanding of the role of subjective prior beliefs and the appropriate form of the likelihood function when exploiting such narrative information. In the new paper that is the topic of this discussion, GKR compare their pre-ferred prior-robust Bayesian inference procedure with an alter-native approach that constructs categorical proxy variables from the narrative information and uses these to estimate impulse responses via instrumental variable (IV) regressions. GKR argue that the proxy approach will likely suffer from weak IV problems when we only have narrative restrictions for a few time periods, as is often the case in practice. To add insult to injury, this cannot be addressed using existing techniques for weak-IV-robust inference in SVARs (Montiel Olea, Stock, and Watson 2021).Inthe following I will make two points. First, the proxy approach to exploiting narrative information has several appeal-ing robustness properties relative to the likelihood approaches of Antolín-Díaz and Rubio-Ramírez (2018) and Giacomini, Kita-gawa, and Read (2021): The proxy approach allows the narrative signals to be imperfect and arrive non-randomly, and further-more, the economic shocks are allowed to be non-invertible (also known as non-fundamental). Second, the weak IV prob-lem that GKR discuss can be overcome by using procedures designed for small samples, such as permutation tests.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"40 1","pages":"1434 - 1437"},"PeriodicalIF":3.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43379665","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