Journal of Econometrics最新文献_第6页

BUMVU estimators

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-03-01 Epub Date: 2025-01-17 DOI: 10.1016/j.jeconom.2024.105942

Aleksey Kolokolov , Roberto Renò , Patrick Zoi

引用次数: 0

FX futures invariance 外汇期货不变性

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.jeconom.2025.106165

Torben G. Andersen , Oleg Bondarenko , Eleni Gousgounis , Esen Onur

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引用次数: 0

Realized drift 实现的漂移

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-03-01 Epub Date: 2024-07-16 DOI: 10.1016/j.jeconom.2024.105813

Sébastien Laurent , Roberto Renò , Shuping Shi

引用次数: 0

Estimation of common factors for microstructure noise and efficient price in a high-frequency dual factor model 高频双因素模型中微观结构噪声和有效价格共同因素的估计

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-03-01 Epub Date: 2023-02-01 DOI: 10.1016/j.jeconom.2022.12.005

Yu-Ning Li , Jia Chen , Oliver Linton

引用次数: 0

High-dimensional conditional factor model 高维条件因子模型

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.jeconom.2026.106203

Zhonghao Fu , Shang Gao , Liangjun Su , Xia Wang

引用次数: 0

Corrigendum to “Robust mutual fund selection with false discovery rate control” [Journal of Econometrics 252 (2025) 106121] “控制错误发现率的稳健共同基金选择”的勘误表[计量经济学杂志252 (2025)106121]

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-03 DOI: 10.1016/j.jeconom.2025.106162

Hongfei Wang , Ping Zhao , Long Feng , Zhaojun Wang

引用次数: 0

Statistical inference for systemic risk-driven portfolio selection 系统性风险驱动投资组合选择的统计推断

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.jeconom.2025.106127

Tsz Chai Fung , Yinhuan Li , Liang Peng , Linyi Qian

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引用次数: 0

Efficient estimation of structural models via sieves 筛子对结构模型的有效估计

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2026-01-16 DOI: 10.1016/j.jeconom.2026.106184

Yao Luo , Peijun Sang

引用次数: 0

Functional semiparametric modeling for nonstationary and periodic time series data 非平稳周期时间序列数据的泛函半参数建模

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-17 DOI: 10.1016/j.jeconom.2025.106149

Shouxia Wang , Hua Liu , Jinhong You , Tao Huang

{"title":"Functional semiparametric modeling for nonstationary and periodic time series data","authors":"Shouxia Wang , Hua Liu , Jinhong You , Tao Huang","doi":"10.1016/j.jeconom.2025.106149","DOIUrl":"10.1016/j.jeconom.2025.106149","url":null,"abstract":"<div><div>Inspired by a real data example illustrating the periodicity in hog price data, this study aims to analyze time series that exhibit an unknown period alongside complex covariate effects. To address these complexities and effectively handle the data structures, we incorporate the partial functional varying-coefficient single-index model into the classical time series decomposition model. We propose a two-stage estimation procedure designed to accurately estimate the unknown periodic component and the associated covariate functions. In the first stage, the unknown period is estimated using a penalized least squares approach, where the covariate functions are approximated via B-splines rather than being ignored. In the second stage, given the estimated period, we employ B-splines to estimate key components, including the amplitude of the periodic component, the varying-coefficient functions, the single-index link function, and the functional slope function. Asymptotic results for the proposed estimators are derived, encompassing the consistency of the period estimator as well as the asymptotic properties of the estimated periodic sequence and covariate functions. Furthermore, we conduct simulations to validate the superior performance of the proposed method and demonstrate its practical applicability through the aforementioned empirical example.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106149"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145577890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large-scale model comparison with fast model confidence sets 快速模型置信集的大尺度模型比较

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.jeconom.2025.106123

Sylvain Barde

{"title":"Large-scale model comparison with fast model confidence sets","authors":"Sylvain Barde","doi":"10.1016/j.jeconom.2025.106123","DOIUrl":"10.1016/j.jeconom.2025.106123","url":null,"abstract":"<div><div>The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations use an elimination approach, where one starts with the full collection of models and successively eliminates the worst performing until the null of equal predictive ability is no longer rejected at a given confidence level. The intuition behind the proposed implementation lies in reversing the process, i.e. starting with a collection of two models and updating both the model rankings and p-values as models are successively added to the collection. The first benefit of this approach is a reduction of one polynomial order in both the time complexity and memory cost of finding the confidence set of a collection of <span><math><mi>M</mi></math></span> models using the <span><math><mi>R</mi></math></span> rule, falling respectively from <span><math><mrow><mi>O</mi><mfenced><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></mfenced></mrow></math></span> to <span><math><mrow><mi>O</mi><mfenced><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></mfenced></mrow></math></span> and from <span><math><mrow><mi>O</mi><mfenced><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></mfenced></mrow></math></span> to <span><math><mrow><mi>O</mi><mfenced><mrow><mi>M</mi></mrow></mfenced></mrow></math></span>. The second key benefit is that it allows for further models to be added at a later point in time, thus enabling collaborative efforts using the model confidence set procedure. The paper proves that this implementation is equivalent to the elimination approach, demonstrates the improved performance on a multivariate GARCH collection consisting of 4800 models, and discusses possible use-cases where this improved performance could prove useful.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106123"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0