Journal of Business & Economic Statistics最新文献_第6页

Spatial Correlation Robust Inference in Linear Regression and Panel Models 线性回归和面板模型的空间相关鲁棒推断

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-23 DOI: 10.1080/07350015.2022.2127737

Ulrich K. Müller, M. Watson

引用次数: 6

Reconciling Trends in Male Earnings Volatility: Evidence from the SIPP Survey and Administrative Data 调和男性收入波动的趋势:来自SIPP调查和行政数据的证据

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-20 DOI: 10.1080/07350015.2022.2126845

Michael Carr, R. Moffitt, E. Wiemers

{"title":"Reconciling Trends in Male Earnings Volatility: Evidence from the SIPP Survey and Administrative Data","authors":"Michael Carr, R. Moffitt, E. Wiemers","doi":"10.1080/07350015.2022.2126845","DOIUrl":"https://doi.org/10.1080/07350015.2022.2126845","url":null,"abstract":"Abstract As part of a set of papers using the same methods and sample selection criteria to estimate trends in male earnings volatility across survey and administrative datasets, we conduct a new investigation of male earnings volatility using data from the Survey of Income and Program Participation (SIPP) survey and SIPP-linked administrative earnings data (SIPP GSF). We find that the level of volatility is higher in the administrative earnings histories in the SIPP GSF than in the SIPP survey but that the trends are similar. Between 1984 and 2012, volatility in the SIPP survey declines slightly while volatility in the SIPP GSF increases slightly. Including imputations due to unit nonresponse in the SIPP survey data increases both the level and upward trend in volatility and poses a challenge for estimating a consistent series in the SIPP survey data. Because the density of low earnings differs considerably across datasets, and volatility may vary across the earnings distribution, we also estimate trends in volatility where we hold the earnings distribution fixed across the two data sources. Differences in the underlying earnings distribution explain much of the difference in the level of and trends in volatility between the SIPP survey and SIPP GSF.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"26 - 32"},"PeriodicalIF":3.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42061814","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

Teacher-to-classroom assignment and student achievement 教师到教室的分配与学生成绩

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-19 DOI: 10.1080/07350015.2022.2126480

Bryan S. Graham, Geert Ridder, Petra Thiemann, Gema Zamarro

引用次数: 0

Changepoint Detection in Heteroscedastic Random Coefficient Autoregressive Models 异方差随机系数自回归模型的变化点检测

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-07 DOI: 10.1080/07350015.2022.2120485

Lajos Horváth, Lorenzo Trapani

引用次数: 6

Corporate Probability of Default: A Single-Index Hazard Model Approach 企业违约概率:单指数风险模型方法

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-07 DOI: 10.1080/07350015.2022.2120484

Shaobo Li, Shaonan Tian, Yan Yu, Xiaorui Zhu, Heng Lian

{"title":"Corporate Probability of Default: A Single-Index Hazard Model Approach","authors":"Shaobo Li, Shaonan Tian, Yan Yu, Xiaorui Zhu, Heng Lian","doi":"10.1080/07350015.2022.2120484","DOIUrl":"https://doi.org/10.1080/07350015.2022.2120484","url":null,"abstract":"Abstract Corporate probability of default (PD) prediction is vitally important for risk management and asset pricing. In search of accurate PD prediction, we propose a flexible yet easy-to-interpret default-prediction single-index hazard model (DSI). By applying it to a comprehensive U.S. corporate bankruptcy database we constructed, we discover an interesting V-shaped relationship, indicating a violation of the common linear hazard specification. Most importantly, the single-index hazard model passes the Hosmer-Lemeshow goodness-of-fit calibration test while neither does a state-of-the-art linear hazard model in finance nor a parametric class of Box-Cox transformation survival models. In an economic value analysis, we find that this may translate to as much as three times of profit compared to the linear hazard model. In model estimation, we adopt a penalized-spline approximation for the unknown function and propose an efficient algorithm. With a diverging number of spline knots, we establish consistency and asymptotic theories for the penalized-spline likelihood estimators. Furthermore, we reexamine the distress risk anomaly, that is, higher financially distressed stocks deliver anomalously lower excess returns. Based on the PDs from the proposed single-index hazard model, we find that the distress risk anomaly has weakened or even disappeared during the extended period.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1288 - 1299"},"PeriodicalIF":3.0,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43809645","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

Generalized Covariance Estimator 广义协方差估计

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-02 DOI: 10.1080/07350015.2022.2120486

C. Gouriéroux, J. Jasiak

引用次数: 0

Testing Stability in Functional Event Observations with an Application to IPO Performance 功能事件观测的稳定性测试及其在IPO业绩中的应用

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-09-01 DOI: 10.1080/07350015.2022.2118127

Lajos Horváth, Zhenya Liu, Gregory Rice, Shixuan Wang, Yaosong Zhan

{"title":"Testing Stability in Functional Event Observations with an Application to IPO Performance","authors":"Lajos Horváth, Zhenya Liu, Gregory Rice, Shixuan Wang, Yaosong Zhan","doi":"10.1080/07350015.2022.2118127","DOIUrl":"https://doi.org/10.1080/07350015.2022.2118127","url":null,"abstract":"Abstract Many sequentially observed functional data objects are available only at the times of certain events. For example, the trajectory of stock prices of companies after their initial public offering (IPO) can be observed when the offering occurs, and the resulting data may be affected by changing circumstances. It is of interest to investigate whether the mean behavior of such functions is stable over time, and if not, to estimate the times at which apparent changes occur. Since the frequency of events may fluctuates over time, we propose a change point analysis that has two steps. In the first step, we segment the series into segments in which the frequency of events is approximately homogeneous using a new binary segmentation procedure for event frequencies. After adjusting the observed curves in each segment based on the frequency of events, we proceed in the second step by developing a method to test for and estimate change points in the mean of the observed functional data objects. We establish the consistency and asymptotic distribution of the change point detector and estimator in both steps, and study their performance using Monte Carlo simulations. An application to IPO performance data illustrates the proposed methods.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1262 - 1273"},"PeriodicalIF":3.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43304873","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

A Scalable Frequentist Model Averaging Method 一种可伸缩的频域模型平均方法

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-23 DOI: 10.1080/07350015.2022.2116442

Rong Zhu, Haiying Wang, Xinyu Zhang, Hua Liang

{"title":"A Scalable Frequentist Model Averaging Method","authors":"Rong Zhu, Haiying Wang, Xinyu Zhang, Hua Liang","doi":"10.1080/07350015.2022.2116442","DOIUrl":"https://doi.org/10.1080/07350015.2022.2116442","url":null,"abstract":"Abstract Frequentist model averaging is an effective technique to handle model uncertainty. However, calculation of the weights for averaging is extremely difficult, if not impossible, even when the dimension of the predictor vector, p, is moderate, because we may have candidate models. The exponential size of the candidate model set makes it difficult to estimate all candidate models, and brings additional numeric errors when calculating the weights. This article proposes a scalable frequentist model averaging method, which is statistically and computationally efficient, to overcome this problem by transforming the original model using the singular value decomposition. The method enables us to find the optimal weights by considering at most p candidate models. We prove that the minimum loss of the scalable model averaging estimator is asymptotically equal to that of the traditional model averaging estimator. We apply the Mallows and Jackknife criteria to the scalable model averaging estimator and prove that they are asymptotically optimal estimators. We further extend the method to the high-dimensional case (i.e., ). Numerical studies illustrate the superiority of the proposed method in terms of both statistical efficiency and computational cost.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"1228 - 1237"},"PeriodicalIF":3.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44128430","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

When are Google data useful to nowcast GDP? An approach via preselection and shrinkage 谷歌数据何时可用于预测国内生产总值？通过预选和收缩的方法

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-22 DOI: 10.1080/07350015.2022.2116025

Laurent Ferrara, Anna Simoni

引用次数: 0

Nonparametric Option Pricing with Generalized Entropic Estimators* 使用广义熵估计器进行非参数期权定价*

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2022-08-22 DOI: 10.1080/07350015.2022.2115499

Caio Almeida, Gustavo Freire, Rafael Azevedo, Kym Ardison

引用次数: 0