Journal of Econometrics最新文献_第2页

Dynamic treatment effect estimation with interactive fixed effects and short panels 基于交互式固定效应和短面板的动态治疗效果估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-10 DOI: 10.1016/j.jeconom.2025.106013

Nicholas L. Brown , Kyle Butts

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引用次数: 0

A simple and computationally trivial estimator for grouped fixed effects models 对于分组固定效应模型的一个简单且计算微不足道的估计器

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-09 DOI: 10.1016/j.jeconom.2025.106011

Martin Mugnier

引用次数: 0

Faster estimation of dynamic discrete choice models using index invertibility 基于指标可逆性的动态离散选择模型的快速估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-09 DOI: 10.1016/j.jeconom.2025.106004

Jackson Bunting , Takuya Ura

{"title":"Faster estimation of dynamic discrete choice models using index invertibility","authors":"Jackson Bunting , Takuya Ura","doi":"10.1016/j.jeconom.2025.106004","DOIUrl":"10.1016/j.jeconom.2025.106004","url":null,"abstract":"<div><div>Many estimators of dynamic discrete choice models with persistent unobserved heterogeneity have desirable statistical properties but are computationally intensive. In this paper we propose a method to quicken estimation for a broad class of dynamic discrete choice problems by exploiting semiparametric index restrictions. Specifically, we propose an estimator for models whose reduced form parameters are invertible functions of one or more linear indices (Ahn et al., 2018) , a property we term index invertibility. We establish that index invertibility implies a set of equality constraints on the model parameters. Our proposed estimator uses the equality constraints to decrease the dimension of the optimization problem, thereby generating computational gains. Our main result shows that the proposed estimator is asymptotically equivalent to the unconstrained, computationally heavy estimator. In addition, we provide a series of results on the number of independent index restrictions on the model parameters, providing theoretical guidance on the extent of computational gains. Finally, we demonstrate the advantages of our approach via Monte Carlo simulations.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106004"},"PeriodicalIF":9.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923089","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

Dimension-agnostic change point detection 与尺寸无关的变化点检测

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-08 DOI: 10.1016/j.jeconom.2025.106012

Hanjia Gao , Runmin Wang , Xiaofeng Shao

{"title":"Dimension-agnostic change point detection","authors":"Hanjia Gao , Runmin Wang , Xiaofeng Shao","doi":"10.1016/j.jeconom.2025.106012","DOIUrl":"10.1016/j.jeconom.2025.106012","url":null,"abstract":"<div><div>Change point testing for high-dimensional data has attracted a lot of attention in statistics, econometrics and machine learning owing to the emergence of high-dimensional data with structural breaks from many fields. In practice, when the dimension is less than the sample size but is not small, it is often unclear whether a method that is tailored to high-dimensional data or simply a classical method that is developed and justified for low-dimensional data is preferred. In addition, the methods designed for low-dimensional data may not work well in the high-dimensional environment and vice versa. In this paper, we propose a dimension-agnostic testing procedure targeting a single change point in the mean of a multivariate weakly dependent time series. Specifically, we can show that the limiting null distribution for our test statistic is the same regardless of the dimensionality and the magnitude of cross-sectional dependence. The power analysis is also conducted to understand the large sample behavior of the proposed test. Through Monte Carlo simulations and a real data illustration, we demonstrate that the finite sample results strongly corroborate the theory and suggest that the proposed test can be used as a benchmark for change-point detection of time series of low, medium, and high dimensions with complex cross-sectional and temporal dependence.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106012"},"PeriodicalIF":9.9,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923088","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

Pairwise valid instruments 成对有效的仪器

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-07 DOI: 10.1016/j.jeconom.2025.106009

Zhenting Sun , Kaspar Wüthrich

引用次数: 0

Inference on quantile processes with a finite number of clusters 有限集群数量的量化过程推理

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105672

Andreas Hagemann

引用次数: 0

Semiparametric approach to estimation of marginal mean effects and marginal quantile effects 估计边际均值效应和边际分位数效应的半参数方法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2023.05.002

Seong-ho Lee , Yanyuan Ma , Elvezio Ronchetti

引用次数: 0

Simultaneous estimation and group identification for network vector autoregressive model with heterogeneous nodes 异构节点网络向量自回归模型的同时估计与组识别

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2023.105564

Xuening Zhu , Ganggang Xu , Jianqing Fan

{"title":"Simultaneous estimation and group identification for network vector autoregressive model with heterogeneous nodes","authors":"Xuening Zhu , Ganggang Xu , Jianqing Fan","doi":"10.1016/j.jeconom.2023.105564","DOIUrl":"10.1016/j.jeconom.2023.105564","url":null,"abstract":"<div><div>Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregressive model with a latent group structure to model heterogeneous dynamic patterns observed from network nodes, for which group-wise network effects and time-invariant fixed-effects can be naturally incorporated. In our framework, the model parameters and network node memberships can be simultaneously estimated by minimizing a least-squares type objective function. In particular, our theoretical investigation allows the number of latent groups <span><math><mi>G</mi></math></span> to be over-specified when achieving the estimation consistency of the model parameters and group memberships, which significantly improves the robustness of the proposed approach. When <span><math><mi>G</mi></math></span> is correctly specified, valid statistical inference can be made for model parameters based on the asymptotic normality of the estimators. A data-driven criterion is developed to consistently identify the true group number for practical use. Extensive simulation studies and two real data examples are used to demonstrate the effectiveness of the proposed methodology.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105564"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455296","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

Multiway empirical likelihood 多元经验似然

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105861

Harold D. Chiang , Yukitoshi Matsushita , Taisuke Otsu

引用次数: 0

Feature-splitting algorithms for ultrahigh dimensional quantile regression 超高维分位数回归的特征分割算法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2023.01.028

Jiawei Wen , Songshan Yang , Christina Dan Wang , Yifan Jiang , Runze Li

{"title":"Feature-splitting algorithms for ultrahigh dimensional quantile regression","authors":"Jiawei Wen , Songshan Yang , Christina Dan Wang , Yifan Jiang , Runze Li","doi":"10.1016/j.jeconom.2023.01.028","DOIUrl":"10.1016/j.jeconom.2023.01.028","url":null,"abstract":"<div><div>This paper is concerned with computational issues related to penalized quantile regression (PQR) with ultrahigh dimensional predictors. Various algorithms have been developed for PQR, but they become ineffective and/or infeasible in the presence of ultrahigh dimensional predictors due to the storage and scalability limitations. The variable updating schema of the feature-splitting algorithm that directly applies the ordinary alternating direction method of multiplier (ADMM) to ultrahigh dimensional PQR may make the algorithm fail to converge. To tackle this hurdle, we propose an efficient and parallelizable algorithm for ultrahigh dimensional PQR based on the three-block ADMM. The compatibility of the proposed algorithm with parallel computing alleviates the storage and scalability limitations of a single machine in the large-scale data processing. We establish the rate of convergence of the newly proposed algorithm. In addition, Monte Carlo simulations are conducted to compare the finite sample performance of the proposed algorithm with that of other existing algorithms. The numerical comparison implies that the proposed algorithm significantly outperforms the existing ones. We further illustrate the proposed algorithm via an empirical analysis of a real-world data set.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105426"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47340418","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