Journal of Econometrics最新文献_第3页

Robust estimation for dynamic spatial autoregression models with nearly optimal rates 具有接近最优速率的动态空间自回归模型的鲁棒估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-24 DOI: 10.1016/j.jeconom.2025.106065

Yin Lu , Chunbai Tao , Di Wang , Gazi Salah Uddin , Libo Wu , Xuening Zhu

引用次数: 0

Sieve estimation of state-varying factor models 状态变因子模型的筛估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-23 DOI: 10.1016/j.jeconom.2025.106064

Liangjun Su , Sainan Jin , Xia Wang

引用次数: 0

A general test for functional inequalities 函数不等式的一般检验

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-15 DOI: 10.1016/j.jeconom.2025.106063

Jia Li , Zhipeng Liao , Wenyu Zhou

引用次数: 0

Layered policy analysis in program evaluation using the marginal treatment effect 边际处理效应在项目评估中的分层政策分析

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-11 DOI: 10.1016/j.jeconom.2025.106060

Ismael Mourifié , Yuanyuan Wan

引用次数: 0

Global identification, estimation and inference of structural impulse response functions in factor models: A unified framework 因子模型中结构冲击响应函数的全局辨识、估计和推断：一个统一的框架

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-10 DOI: 10.1016/j.jeconom.2025.106057

Xu Han

引用次数: 0

Bernstein-type inequalities and nonparametric estimation under near-epoch dependence 近历元依赖下的bernstein型不等式和非参数估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-09 DOI: 10.1016/j.jeconom.2025.106054

Zihao Yuan , Martin Spindler

{"title":"Bernstein-type inequalities and nonparametric estimation under near-epoch dependence","authors":"Zihao Yuan , Martin Spindler","doi":"10.1016/j.jeconom.2025.106054","DOIUrl":"10.1016/j.jeconom.2025.106054","url":null,"abstract":"<div><div>The main contributions of this paper are twofold. First, we derive Bernstein-type inequalities for irregularly spaced data under near-epoch dependent (NED) conditions and deterministic domain-expanding-infill (DEI) asymptotics. By introducing the concept of “effective dimension” to describe the geometric structure of sampled locations, we illustrate – unlike previous research – that the sharpness of these inequalities is affected by this effective dimension. To our knowledge, ours is the first study to report this phenomenon and show Bernstein-type inequalities under deterministic DEI asymptotics. This work represents a direct generalization of the work of Xu and Lee (2018), thus marking an important contribution to the topic. As a corollary, we derive a Bernstein-type inequality for irregularly spaced <span><math><mi>α</mi></math></span>-mixing random fields under DEI asymptotics. Our second contribution is to apply these inequalities to explore the attainability of optimal convergence rates for the local linear conditional mean estimator under algebraic NED conditions. Our results illustrate how the effective dimension affects assumptions of dependence. This finding refines the results of Jenish (2012) and extends the work of Hansen (2008), Vogt (2012), Chen and Christensen (2015) and Li, Lu, and Linton (2012).</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106054"},"PeriodicalIF":9.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579228","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

Fast computation of exact confidence intervals for randomized experiments with binary outcomes 具有二元结果的随机实验精确置信区间的快速计算

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-09 DOI: 10.1016/j.jeconom.2025.106056

P.M. Aronow , Haoge Chang , Patrick Lopatto

{"title":"Fast computation of exact confidence intervals for randomized experiments with binary outcomes","authors":"P.M. Aronow , Haoge Chang , Patrick Lopatto","doi":"10.1016/j.jeconom.2025.106056","DOIUrl":"10.1016/j.jeconom.2025.106056","url":null,"abstract":"<div><div>Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests. This approach requires minimal assumptions and is valid for all sample sizes, as it does not rely on large-sample approximations such as those implied by the central limit theorem. We show that these confidence intervals can be found in <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> permutation tests in the case of balanced designs, where the treatment and control groups have equal sizes, and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> permutation tests in the general case. Prior to this work, the most efficient known constructions required <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> such tests in the balanced case (Li and Ding, 2016), and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> tests in the general case (Rigdon and Hudgens, 2015). Our results thus facilitate exact inference as a viable option for randomized experiments far larger than those accessible by previous methods. We also generalize our construction to produce confidence intervals for other causal estimands, including the relative risk ratio and odds ratio, yielding similar computational gains.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106056"},"PeriodicalIF":9.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579227","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

High frequency factor analysis with partially observable factors 部分可观察因子的高频因子分析

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-08 DOI: 10.1016/j.jeconom.2025.106058

Dachuan Chen , Wenqi Lu , Siyu Xie

{"title":"High frequency factor analysis with partially observable factors","authors":"Dachuan Chen , Wenqi Lu , Siyu Xie","doi":"10.1016/j.jeconom.2025.106058","DOIUrl":"10.1016/j.jeconom.2025.106058","url":null,"abstract":"<div><div>This paper considers a novel factor structure – <em>Partially Observable Factor Model</em> – where both observable factors and latent factors exist in the model simultaneously. Such factor structure can make sure both interpretability and goodness-of-fit at the same time. Necessary estimation methodologies for this partially observable factor model are developed in this paper for the high frequency data. The proposed estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously.</div><div>When the observable factors are exogenous, we provide the estimation theory for the integrated eigenvalues of the residual covariance matrix, which including the bias-corrected estimator, central limit theorem and asymptotic variance estimator. As a result, the asymptotic normality of the bias-corrected estimator can be applied to test the existence of the latent factors.</div><div>When the observable factors are endogenous, we propose a novel framework of high frequency unsupervised exogenous component learning (HF-UECL), which can help people quantify the contributions of the observable factors into the latent factors. This is the first work on high frequency instrumental variables, and it can be regard as a necessary and non-trivial extension of the Projected-PCA in the world of continuous-time model. Statistical inferences have been established for the loadings of the observable factors onto the latent factors.</div><div>Monte Carlo simulation demonstrates the validity of our estimation methodologies. Empirical study demonstrates that (i) in the exogenous setting, the latent factors significantly exist in the residual process of the high frequency regression; (ii) in the endogenous setting, the correlations between the observable factors and latent factors do exist significantly.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106058"},"PeriodicalIF":9.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572708","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

Bias correction for quantile regression estimators 分位数回归估计的偏差校正

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-07-05 DOI: 10.1016/j.jeconom.2025.106053

Grigory Franguridi , Bulat Gafarov , Kaspar Wüthrich

引用次数: 0

Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance 利用马氏距离的最佳选择再随机化的渐近理论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-06-25 DOI: 10.1016/j.jeconom.2025.106049

Yuhao Wang , Xinran Li

{"title":"Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance","authors":"Yuhao Wang , Xinran Li","doi":"10.1016/j.jeconom.2025.106049","DOIUrl":"10.1016/j.jeconom.2025.106049","url":null,"abstract":"<div><div>Rerandomization, a design that utilizes pretreatment covariates and improves their balance between different treatment groups, has received attention recently in both theory and practice. From a survey by Bruhn and McKenzie (2009), there are at least two types of rerandomization that are used in practice: the first rerandomizes the treatment assignment until covariate imbalance is below a prespecified threshold; the second randomizes the treatment assignment multiple times and chooses the one with the best covariate balance. In this paper we will consider the second type of rerandomization, namely the best-choice rerandomization, whose theory and inference are still lacking in the literature. In particular, we will focus on the best-choice rerandomization that uses the Mahalanobis distance to measure covariate imbalance, which is one of the most commonly used imbalance measure for multivariate covariates and is invariant to affine transformations of covariates. We will study the large-sample repeatedly sampling properties of the best-choice rerandomization, allowing both the number of covariates and the number of tried complete randomizations to increase with the sample size. We show that the asymptotic distribution of the difference-in-means estimator is more concentrated around the true average treatment effect under rerandomization than under the complete randomization, and propose large-sample accurate confidence intervals for rerandomization that are shorter than that for the completely randomized experiment. We further demonstrate that, with moderate number of covariates and with the number of tried randomizations increasing polynomially with the sample size, the best-choice rerandomization can achieve the ideally optimal precision that one can expect even with perfectly balanced covariates. The developed theory and methods for rerandomization are also illustrated using real field experiments.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106049"},"PeriodicalIF":9.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479985","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