Journal of Econometrics最新文献_第10页

Exogenous consideration and extended random utility 外生考虑与扩展随机效用

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-29 DOI: 10.1016/j.jeconom.2025.106166

Roy Allen

引用次数: 0

Inference for time-varying factor models under local stationarity 局部平稳性下时变因子模型的推理

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-05 DOI: 10.1016/j.jeconom.2025.106154

Weichi Wu , Zhou Zhou , Yongmiao Hong

{"title":"Inference for time-varying factor models under local stationarity","authors":"Weichi Wu , Zhou Zhou , Yongmiao Hong","doi":"10.1016/j.jeconom.2025.106154","DOIUrl":"10.1016/j.jeconom.2025.106154","url":null,"abstract":"<div><div>This paper considers estimation of and testing for a class of locally stationary time series factor models with evolutionary dynamics, where the entries and dimension of the factor loading matrix are allowed to vary with time while the factors and idiosyncratic components are locally stationary. We propose an adaptive sieve estimator for the span of the time-varying loading matrix of a locally stationary factor process. A uniformly consistent estimator of the effective number of factors is developed via eigenanalysis of a non-negative definite time-varying matrix. We also propose a possibly high-dimensional bootstrap test for the hypothesis of constant factor loadings by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. This test avoids the assumption that factors and idiosyncratic errors are stationary or the covariance matrix of factors is time-invariant. Our results cover both cases of white noise idiosyncratic errors and serially correlated idiosyncratic errors. We examine the finite sample performance of our proposed estimator and test via simulation studies and real data analysis.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106154"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690428","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

Identification of incomplete information allocation-transfer games in monotone equilibrium 单调均衡下不完全信息分配-转移对策的辨识

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-06 DOI: 10.1016/j.jeconom.2025.106104

Brendan Kline

引用次数: 0

Estimation and inference for CP tensor factor models CP张量因子模型的估计与推理

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-18 DOI: 10.1016/j.jeconom.2025.106167

Bin Chen , Yuefeng Han , Qiyang Yu

引用次数: 0

Identification and estimation of partial effects in nonlinear semiparametric panel models 非线性半参数面板模型部分效应的辨识与估计

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-11-01 Epub Date: 2024-09-28 DOI: 10.1016/j.jeconom.2024.105860

Laura Liu , Alexandre Poirier , Ji-Liang Shiu

引用次数: 0

Shrinkage methods for treatment choice 收缩处理方法的选择

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-11-01 Epub Date: 2025-10-16 DOI: 10.1016/j.jeconom.2025.106117

Takuya Ishihara , Daisuke Kurisu

{"title":"Shrinkage methods for treatment choice","authors":"Takuya Ishihara , Daisuke Kurisu","doi":"10.1016/j.jeconom.2025.106117","DOIUrl":"10.1016/j.jeconom.2025.106117","url":null,"abstract":"<div><div>This study examines the problem of determining whether to treat individuals based on observed covariates. The most common decision rule is the conditional empirical success (CES) rule proposed by Manski (2004), which assigns individuals to treatments that yield the best experimental outcomes conditional on the observed covariates. Conversely, using shrinkage estimators, which shrink unbiased but noisy preliminary estimates toward the average of these estimates, is a common approach in statistical estimation problems because it is well-known that shrinkage estimators may have smaller mean squared errors than unshrunk estimators. Inspired by this idea, we propose a computationally tractable shrinkage rule that selects the shrinkage factor by minimizing an upper bound of the maximum regret. Then, we compare the maximum regret of the proposed shrinkage rule with those of the CES and pooling rules when the space of conditional average treatment effects (CATEs) is correctly specified or misspecified. Our theoretical results demonstrate that the shrinkage rule performs well in many cases and these findings are further supported by numerical experiments. Specifically, we show that the maximum regret of the shrinkage rule can be strictly smaller than those of the CES and pooling rules in certain cases when the space of CATEs is correctly specified. In addition, we find that the shrinkage rule is robust against misspecification of the space of CATEs. Finally, we apply our method to experimental data from the National Job Training Partnership Act Study.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106117"},"PeriodicalIF":4.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323901","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

Quantile graphical models: Prediction and conditional independence with applications to systemic risk 分位数图形模型：预测和条件独立性与系统风险的应用

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-11-01 Epub Date: 2025-10-09 DOI: 10.1016/j.jeconom.2025.106100

Alexandre Belloni , Mingli Chen , Victor Chernozhukov

{"title":"Quantile graphical models: Prediction and conditional independence with applications to systemic risk","authors":"Alexandre Belloni , Mingli Chen , Victor Chernozhukov","doi":"10.1016/j.jeconom.2025.106100","DOIUrl":"10.1016/j.jeconom.2025.106100","url":null,"abstract":"<div><div>We propose two types of Quantile Graphical Models: (i) Conditional Independence Quantile Graphical Models (CIQGMs) characterize the conditional independence by evaluating the distributional dependence structure at each quantile index, as such, those can be used for validation of the graph structure in the causal graphical models; (ii) Prediction Quantile Graphical Models (PQGMs) characterize the statistical dependencies through the graphs of the best linear predictors under asymmetric loss functions. PQGMs make weaker assumptions than CIQGMs as they allow for misspecification. One advantage of these models is that we can apply them to large collections of variables driven by non-Gaussian and non-separable shocks. Because of QGMs’ ability to handle large collections of variables and focus on specific parts of the distributions, we could apply them to quantify tail interdependence. The resulting tail risk network can be used for measuring systemic risk contributions that help make inroads in understanding international financial contagion and dependence structures of returns under downside market movements.</div><div>We develop estimation and inference methods focusing on the high-dimensional case, where the number of nodes in the graph is large as compared to the number of observations. For CIQGMs, these results include valid simultaneous choices of penalty functions, uniform rates of convergence, and confidence regions that are simultaneously valid. We also derive analogous results for PQGMs, which include new results for penalized quantile regressions in high-dimensional settings to handle misspecification, many controls, and a continuum of additional conditioning events.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106100"},"PeriodicalIF":4.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266918","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

Nonparametric regression under cluster sampling 聚类抽样下的非参数回归

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-11-01 Epub Date: 2025-09-22 DOI: 10.1016/j.jeconom.2025.106102

Yuya Shimizu

引用次数: 0

Weighted residual empirical processes, martingale transformations, and model specification tests for regressions with diverging number of parameters 加权残差经验过程，鞅变换，和模型规格测试的回归与发散数的参数

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-11-01 Epub Date: 2025-10-13 DOI: 10.1016/j.jeconom.2025.106113

Falong Tan , Xu Guo , Lixing Zhu

{"title":"Weighted residual empirical processes, martingale transformations, and model specification tests for regressions with diverging number of parameters","authors":"Falong Tan , Xu Guo , Lixing Zhu","doi":"10.1016/j.jeconom.2025.106113","DOIUrl":"10.1016/j.jeconom.2025.106113","url":null,"abstract":"<div><div>This paper explores hypothesis testing for the parametric forms of the mean and variance functions in regression models under diverging-dimension settings. To mitigate the curse of dimensionality, we introduce weighted residual empirical process-based tests, both with and without martingale transformations. The asymptotic properties of these tests are derived from the behavior of weighted residual empirical processes and their martingale transformations under the null and alternative hypotheses. The proposed tests without martingale transformations achieve the fastest possible rate of detecting local alternatives, specifically of order <span><math><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></math></span>, which is unaffected by dimensionality. However, these tests are not asymptotically distribution-free. To address this limitation, we propose a smooth residual bootstrap approximation and establish its validity in diverging-dimension settings. In contrast, tests incorporating martingale transformations are asymptotically distribution-free but exhibit an unexpected limitation: they can only detect local alternatives converging to the null at a much slower rate of order <span><math><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mn>4</mn></mrow></msup></math></span>, which remains independent of dimensionality. This finding reveals a theoretical advantage in the power of tests based on weighted residual empirical process without martingale transformations over their martingale-transformed counterparts, challenging the conventional wisdom of existing asymptotically distribution-free tests based on martingale transformations. To validate our approach, we conduct simulation studies and apply the proposed tests to a real-world dataset, demonstrating their practical effectiveness.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106113"},"PeriodicalIF":4.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323902","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

GMM estimation with Brownian kernels applied to income inequality measurement 布朗核GMM估计在收入不平等测量中的应用

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-11-01 Epub Date: 2025-10-14 DOI: 10.1016/j.jeconom.2025.106110

Jin Seo Cho , Peter C.B. Phillips

{"title":"GMM estimation with Brownian kernels applied to income inequality measurement","authors":"Jin Seo Cho , Peter C.B. Phillips","doi":"10.1016/j.jeconom.2025.106110","DOIUrl":"10.1016/j.jeconom.2025.106110","url":null,"abstract":"<div><div>In GMM estimation, it is well known that if the moment dimension grows with the sample size, the asymptotics of GMM differ from the standard finite dimensional case. The present work examines the asymptotic properties of infinite dimensional GMM estimation when the weight matrix is formed by inverting Brownian motion or Brownian bridge covariance kernels. These kernels arise in econometric work such as minimum Cramér–von Mises distance estimation when testing distributional specification. The properties of GMM estimation are studied under different environments where the moment conditions converge to a smooth Gaussian or non-differentiable Gaussian process. Conditions are also developed for testing the validity of the moment conditions by means of a suitably constructed <span><math><mi>J</mi></math></span>-statistic. In case these conditions are invalid we propose another test called the <span><math><mi>U</mi></math></span>-test. As an empirical application of these infinite dimensional GMM procedures the evolution of cohort labor income inequality indices is studied using the Continuous Work History Sample database. The findings show that labor income inequality indices are maximized at early career years, implying that economic policies to reduce income inequality should be more effective when designed for workers at an early stage in their career cycles.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106110"},"PeriodicalIF":4.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324449","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