发布求助
文献互助 智能选刊 最新文献

Econometrics and Statistics最新文献

筛选
英文 中文
A Bayesian flexible model for testing Granger causality 检验格兰杰因果关系的贝叶斯灵活模型
IF 1.9
Econometrics and Statistics Pub Date : 2024-08-03 DOI: 10.1016/j.ecosta.2024.08.001
Iván Gutiérrez, Danilo Alvares, Luis Gutiérrez
{"title":"A Bayesian flexible model for testing Granger causality","authors":"Iván Gutiérrez, Danilo Alvares, Luis Gutiérrez","doi":"10.1016/j.ecosta.2024.08.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.08.001","url":null,"abstract":"A new Bayesian hypothesis testing procedure for evaluating the Granger causality between two or more time series is proposed. The test is based on a flexible model for the joint evolution of multiple series, where a latent binary matrix indicates whether there is a Granger-causal relationship between such time series. The model is specified through a dependent Geometric stick-breaking process that generalizes the standard parametric Gaussian vector autoregression model, whereas the prior distribution of the latent matrix ensures a multiple testing correction. A Monte Carlo simulation study is provided for comparing the robustness of the proposed hypothesis test with state-of-the-art alternatives. The results show that this proposal performs better than competing approaches. Finally, the new test is applied to real economic data.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"3 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Differentially Private Goodness-of-Fit Tests for Continuous Variables 连续变量的差分私有拟合优度测试
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.09.007
Seung Woo Kwak , Jeongyoun Ahn , Jaewoo Lee , Cheolwoo Park
{"title":"Differentially Private Goodness-of-Fit Tests for Continuous Variables","authors":"Seung Woo Kwak ,&nbsp;Jeongyoun Ahn ,&nbsp;Jaewoo Lee ,&nbsp;Cheolwoo Park","doi":"10.1016/j.ecosta.2021.09.007","DOIUrl":"10.1016/j.ecosta.2021.09.007","url":null,"abstract":"<div><p>Data privacy is a growing concern in modern data analyses as more and more types of information about individuals are collected and shared. Statistical analysis in consideration of privacy is thus becoming an exciting area of research. Differential privacy can provide a means by which one can measure the stochastic risk of violating the privacy of individuals that can result from conducting an analysis, such as a simple query from a database and a hypothesis test. The main interest of the work is a goodness-of-fit test that compares the sampled data to a known distribution. Many differentially private goodness-of-fit tests have been proposed for discrete random variables, but little work has been done for continuous variables. The objective is to review some existing tests that guarantee differential privacy for discrete random variables, and to propose an extension to continuous cases via a discretization process. The proposed test procedures are demonstrated through simulated examples and applied to the Household Financial Welfare Survey of South Korea in 2018.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 81-99"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74095071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification 非线性边际 Logistic 回归的半参数平均化和时间序列分类预测
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.11.001
Rong Peng , Zudi Lu
{"title":"Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification","authors":"Rong Peng ,&nbsp;Zudi Lu","doi":"10.1016/j.ecosta.2021.11.001","DOIUrl":"10.1016/j.ecosta.2021.11.001","url":null,"abstract":"<div><p>Binary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of <span><math><mi>β</mi></math></span>-mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 19-37"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86385087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data 零膨胀纵向计数数据的条件量子函数
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.09.003
Carlos Lamarche , Xuan Shi , Derek S. Young
{"title":"Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data","authors":"Carlos Lamarche ,&nbsp;Xuan Shi ,&nbsp;Derek S. Young","doi":"10.1016/j.ecosta.2021.09.003","DOIUrl":"10.1016/j.ecosta.2021.09.003","url":null,"abstract":"<div><p>The identification and estimation of conditional quantile functions for count responses using longitudinal data are considered. The approach is based on a continuous approximation to distribution functions for count responses within a class of parametric models that are commonly employed. It is first shown that conditional quantile functions for count responses are identified in zero-inflated models with subject heterogeneity. Then, a simple three-step approach is developed to estimate the effects of covariates on the quantiles of the response variable. A simulation study is presented to show the small sample performance of the estimator. Finally, the advantages of the proposed estimator in relation to some existing methods is illustrated by estimating a model of annual visits to physicians using data from a health insurance experiment.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 49-65"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86565670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bias correction for Vandermonde low-rank approximation 范德蒙德低秩近似的偏差修正
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.09.001
Antonio Fazzi , Alexander Kukush , Ivan Markovsky
{"title":"Bias correction for Vandermonde low-rank approximation","authors":"Antonio Fazzi ,&nbsp;Alexander Kukush ,&nbsp;Ivan Markovsky","doi":"10.1016/j.ecosta.2021.09.001","DOIUrl":"10.1016/j.ecosta.2021.09.001","url":null,"abstract":"<div><p>The low-rank approximation problem, that is the problem of approximating a given matrix with a matrix of lower rank, appears in many scientific fields. In some applications the given matrix is structured and the approximation is required to have the same structure. Examples of linear structures are Hankel, Toeplitz, and Sylvester. Currently, there are only a few results for nonlinearly structured low-rank approximation problems. The problem of Vandermonde structured low-rank approximation is considered. The high condition number of the Vandermonde matrix, in combination with the noise in the data, makes the problem challenging. A numerical method based on a bias correction procedure is proposed and its properties are demonstrated by simulation. The performance of the method is illustrated on numerical results.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 38-48"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74509464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Edgeworth expansions for multivariate random sums 多元随机和的埃奇沃斯展开式
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.04.005
Farrukh Javed , Nicola Loperfido , Stepan Mazur
{"title":"Edgeworth expansions for multivariate random sums","authors":"Farrukh Javed ,&nbsp;Nicola Loperfido ,&nbsp;Stepan Mazur","doi":"10.1016/j.ecosta.2021.04.005","DOIUrl":"10.1016/j.ecosta.2021.04.005","url":null,"abstract":"<div><p>The sum of a random number of independent and identically distributed random vectors has a distribution which is not analytically tractable, in the general case. The problem has been addressed by means of asymptotic approximations embedding the number of summands in a stochastically increasing sequence. Another approach relies on fitting flexible and tractable parametric, multivariate distributions, as for example finite mixtures. Both approaches are investigated within the framework of Edgeworth expansions. A general formula for the fourth-order cumulants of the random sum of independent and identically distributed random vectors is derived and it is shown that the above mentioned asymptotic approach does not necessarily lead to valid asymptotic normal approximations. The problem is addressed by means of Edgeworth expansions. Both theoretical and empirical results suggest that mixtures of two multivariate normal distributions with proportional covariance matrices satisfactorily fit data generated from random sums where the counting random variable and the random summands are Poisson and multivariate skew-normal, respectively.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 66-80"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86545405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables 大型宏观变量面板中线性降维方法的预测近似性
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.10.007
Alessandro Barbarino , Efstathia Bura
{"title":"Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables","authors":"Alessandro Barbarino ,&nbsp;Efstathia Bura","doi":"10.1016/j.ecosta.2021.10.007","DOIUrl":"10.1016/j.ecosta.2021.10.007","url":null,"abstract":"<div><p>In an extensive pseudo out-of-sample horserace, classical estimators (dynamic factor models, RIDGE and partial least squares regression) and the novel to forecasting, Regularized Sliced Inverse Regression, exhibit almost near-equivalent forecasting accuracy in a large panel of macroeconomic variables across targets, horizons and subsamples. This finding motivates the theoretical contributions in this paper. Most widely used linear dimension reduction methods are shown to solve closely related maximization problems with solutions that can be decomposed in <em>signal</em> and <em>scaling</em> components. They are organized under a common scheme that sheds light on their commonalities and differences as well as on their functionality. Regularized Sliced Inverse Regression delivers the most parsimonious forecast model and obtains the greatest reduction of the complexity of the forecasting problem. Nevertheless, the study’s findings are that (a) the intrinsic relationship between forecast target and the other macroseries in the panel is linear and (b) targeting contributes in reducing the complexity of modeling yet does not induce significant gains in macroeconomic forecasting accuracy.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 1-18"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80948953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multivariate Count Time Series Modelling 多变量计数时间序列建模
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.11.006
Konstantinos Fokianos
{"title":"Multivariate Count Time Series Modelling","authors":"Konstantinos Fokianos","doi":"10.1016/j.ecosta.2021.11.006","DOIUrl":"10.1016/j.ecosta.2021.11.006","url":null,"abstract":"<div><p>Autoregressive models are reviewed for the analysis of multivariate count time series. A particular topic of interest which is discussed in detail is that of the choice of a suitable distribution for a vectors of count random variables. The focus is on three main approaches taken for multivariate count time series analysis: (a) integer autoregressive processes, (b) parameter-driven models and (c) observation-driven models. The aim is to highlight some recent methodological developments and propose some potentially useful research topics.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 100-116"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89113611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spatial-Temporal Analysis of Multi-Subject Functional Magnetic Resonance Imaging Data 多受试者功能磁共振成像数据的时空分析
IF 2
Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.02.006
Tingting Zhang , Minh Pham , Guofen Yan , Yaotian Wang , Sara Medina-DeVilliers , James A. Coan
{"title":"Spatial-Temporal Analysis of Multi-Subject Functional Magnetic Resonance Imaging Data","authors":"Tingting Zhang ,&nbsp;Minh Pham ,&nbsp;Guofen Yan ,&nbsp;Yaotian Wang ,&nbsp;Sara Medina-DeVilliers ,&nbsp;James A. Coan","doi":"10.1016/j.ecosta.2021.02.006","DOIUrl":"10.1016/j.ecosta.2021.02.006","url":null,"abstract":"<div><p>Functional magnetic resonance imaging (fMRI) is one of the most popular neuroimaging technologies used in human brain studies. However, fMRI data analysis faces several challenges, including intensive computation due to the massive data size and large estimation errors due to a low signal-to-noise ratio of the data. A new statistical model and a computational algorithm are proposed to address these challenges. Specifically, a new multi-subject general linear model is built for stimulus-evoked fMRI data. The new model assumes that brain responses to stimuli at different brain regions of various subjects fall into a low-rank structure and can be represented by a few principal functions. Therefore, the new model enables combining data information across subjects and regions to evaluate subject-specific and region-specific brain activity. Two optimization functions and a new fast-to-compute algorithm are developed to analyze multi-subject stimulus-evoked fMRI data and address two research questions of a broad interest in psychology: evaluating every subject’s brain responses to different stimuli and identifying brain regions responsive to the stimuli. Both simulation and real data analysis are conducted to show that the new method can outperform existing methods by providing more efficient estimates of brain activity.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 117-129"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75841092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Estimating the Output Gap with High-Dimensional Time Series 利用高维时间序列估算产出缺口
IF 1.9
Econometrics and Statistics Pub Date : 2024-06-26 DOI: 10.1016/j.ecosta.2024.06.004
A. Giovannelli, T. Proietti
{"title":"Estimating the Output Gap with High-Dimensional Time Series","authors":"A. Giovannelli, T. Proietti","doi":"10.1016/j.ecosta.2024.06.004","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.06.004","url":null,"abstract":"The output gap measures the deviation of observed output from its potential, thereby defining imbalances in the real economy that affect utilization of resources and price inflation. A novel estimator of the output gap is proposed. It is based on a dynamic factor model that extracts from a high-dimensional set of time series the common component of a stationary transformation of the individual series. The latter results from the application of a nonlinear gap filter, such that for each of the individual time series the gap filter removes from the current value the historical local maximum, which in turn defines the potential. The smooth generalized principal components are extracted and the resulting common components are aggregated into a global output gap measure. An application is presented dealing with the U.S. industrial sector, where the proposed measure is constructed using the disaggregated market and industry groups time series. An evaluation of its external validity is conducted in comparison to alternative measures.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"27 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信